Aijrstem vol2 print 1 by iasir journals

ISSN (PRINT): 2328-3491 ISSN (ONLINE): 2328-3580 ISSN (CD-ROM): 2328-3629

Issue 7, Volume 1, 2 & 3 June-August, 2014

American International Journal of Research in Science, Technology, Engineering & Mathematics

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, aijrstem@gmail.com

PREFACE We are delighted to welcome you to the seventh issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM). In recent years, advances in science, technology, engineering, and mathematics have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. AIJRSTEM is publishing high-quality, peer-reviewed papers covering topics such as Computer and computational sciences, Physics, Chemistry, Mathematics, Applied mathematics, Biochemistry, Robotics, Statistics, Electrical & Electronics engineering, Mechanical & Industrial engineering, Civil Engineering, Aerospace engineering, Chemical engineering, Astrophysics, Nanotechnology, Acoustical engineering, Atmospheric

sciences,

Biological

sciences,

Education

and

Human

Resources,

Environmental research and education, Geosciences, Social, Behavioral and Economic sciences, Geospatial technology, Cyber security, Transportation, Energy and Power, Healthcare, Hospitality, Medical and dental sciences, Marine sciences, Renewable sources of energy, Green technologies, Theory and models and other closely related fields in the discipline of Science, Technology, Engineering & Mathematics. The editorial board of AIJRSTEM is composed of members of the Teachers & Researchers community who have expertise in the fields of Science, Technology, Engineering & Mathematics in order to develop and implement widespread expansion of highâ&#x20AC;?quality common standards and assessments. These fields are the pillars of growth in our modern society and have a wider impact on our daily lives with infinite opportunities in a global marketplace. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to science, technology, engineering & mathematics. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the AIJRSTEM for entrusting us with the important job. We are thankful to the members of the AIJRSTEM editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this seventh issue, we received 143 research papers and out of which only 52 research papers are published in three volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of science, technology, engineering & mathematics.

This issue of the AIJRSTEM has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in science, technology, engineering & mathematics and may open new area for research and development. We hope you will enjoy this seventh issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM), ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 (June-August, 2014, Issue 7, Volume 1, 2 & 3). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Computer and computational sciences  Physics  Chemistry  Mathematics  Actuarial sciences  Applied mathematics  Biochemistry, Bioinformatics  Robotics  Computer engineering  Statistics  Electrical engineering & Electronics  Mechanical engineering  Industrial engineering  Information sciences  Civil Engineering  Aerospace engineering  Chemical engineering  Sports sciences  Military sciences  Astrophysics & Astronomy  Optics  Nanotechnology  Nuclear physics  Operations research  Neurobiology & Biomechanics  Acoustical engineering  Geographic information systems  Atmospheric sciences  Educational/Instructional technology  Biological sciences  Education and Human resource  Extreme engineering applications  Environmental research and education  Geosciences  Social, Behavioral and Economic sciences  Advanced manufacturing technology  Automotive & Construction  Geospatial technology  Cyber security  Transportation  Energy and Power  Healthcare & Hospitality  Medical and dental sciences  Pesticides  Marine and thermal sciences  Pollution  Renewable sources of energy  Industrial pollution control  Hazardous and e-waste management  Green technologies  Artificial/computational intelligence  Theory and models

TABLE OF CONTENTS (June-August, 2014, Issue 7, Volume 1, 2 & 3) Issue 7, Volume 1 Paper Code

Paper Title

Page No.

AIJRSTEM 14-505

Performance Improvisation of Conventional Grape Drying Method by Introducing Forced Air Exhaust Mr. Amol Ubale, Dr. Dilip R. Pangavhane, Dr. Arundhati Warke

01-05

AIJRSTEM 14-512

Fuzzy Candlestick based Stock Market Trading System using Hammer Pattern Partha Roy, Ramesh Kumar, Sanjay Sharma

06-10

AIJRSTEM 14-516

A Review of Space time trellis codes with ideal beamforming for quasi-static Rayleigh fading channel Ramandeep Singh, Amandeep Singh, Dr. Charanjit Singh

11-15

AIJRSTEM 14-517

Predicting Crack Width in Rectangular Ground Supported Reservoir Subject to Seismic Loading Using Radial Basis Neural Networks: RC & FRC Wall Tulesh.N.Patel, S.A. Vasanwala, C.D. Modhera

16-21

AIJRSTEM 14-518

Design and Analysis of Hybrid Current/Voltage CMOS SRAM Sense Amplifier with Offset Cancellation Karishma Bajaj, Manjit Kaur, Gurmohan Singh

22-29

AIJRSTEM 14-519

Application of Craig Bampton Technique in Flutter Analysis of a Sounding Rocket using MSC NASTRAN Anwar Rajeev, Jobin Joy

30-34

AIJRSTEM 14-522

Bleaching Of Sunflower Waste Oil by Absorption On Activated Carbon And Improved By Ozonisation Sivasakthivel. S and Nandini.N

35-39

AIJRSTEM 14-527

Multi-objective Optimization of Magnetic-field-assisted EDM Process using Non-dominated Sorted Genetic Algorithm Vijay Kumar S. Jatti and T.P.Singh

40-44

AIJRSTEM 14-528

A Comparative Study of Mixed Quadrature Rule With the Compound Quadrature rules Manoj Kumar Hota, Prasanta Kumar Mohanty, Saumya Ranjan Jena

45-52

AIJRSTEM 14-529

Comparative Analysis of Alternative and Conventional Regenerative Gas Turbine Cycle Using Entropy Generation Approach Rajeev Dewangan

53-56

AIJRSTEM 14-530

Comparative study of the effect due to slurry concentration and normal load on wear life of Mild steel and hard faced Martensitic stainless steel Apurv Choubey

57-62

AIJRSTEM 14-532

A Study on The Shortest Queuing Model With Jockeying M. Reni Sagayaraj, S. Anand Gnana Selvam, R.Reynald Susainathan, A.Charlas Sagayaraj

63-37

AIJRSTEM 14-533

Performance Analysis of Dielectric Radiation with Sporadic Metallic Sheet Pratik Kumar Mangardaita, P. Jha, S. N. Singh

68-71

AIJRSTEM 14-534

Fracture Strength Evaluation of AA 2219-T87 Weldment using Artificial Neural Network S.Rajakumar and N.Murugan

72-79

AIJRSTEM 14-537

Review of DWDM Technology in Optical Communication Dr. Dalveer Kaur, Er. Vikrant Sharma

80-84

AIJRSTEM 14-540

Modelling and Simulation of Low Power SRAM Cell with Improved Read Speed at 45nm Technology PN Vamsi Kiran, Anurag Mondal

85-89

AIJRSTEM 14-543

Combating Resource Consumption and Byzantine Attacks in MANET through Enhanced CBDS Technique Muskan Sharma, Chander Prabha, Amit Chabbra

90-94

Issue 7, Volume 2 Paper Code

Paper Title

Page No.

AIJRSTEM 14-549

Optimization of Configuration of Inertial Propulsion System for Future Space Application Anand G, Jobin Joy, K Vijayan

95-100

AIJRSTEM 14-550

Denoising Framework Using Switching Bilateral Filter Srinevasan.M, Balajee.B

101-104

AIJRSTEM 14-551

Land Reclamation Using Prefabricated Vertical Drains (PVD) In Port of MOMBASA Fred M. Machine, Kiptanui Arap Too

105-110

AIJRSTEM 14-554

Image Encryption using Chaotic Maps and DNA Addition Operation and Noise Effects on it Manisha Raj, Shelly Garg

111-116

AIJRSTEM 14-555

Shortest Path Problem under Rough and Uncertain Environment Sagarika Biswal, S. P. Mohanty

117-124

AIJRSTEM 14-557

A Note on Volume of Parallelopiped Sameen Ahmed and Sandeep Suman

125-126

AIJRSTEM 14-560

Characterization of alumina catalyst in the catalytic fuel reformer M. Kannan and Dr. C.G. Saravanan

127-131

AIJRSTEM 14-561

A direct mathematical method to calculate the efficiencies of 4pNaI (Tl) scintillation detector Salam Noureldine and Yousef H. Ajeeb

132-138

AIJRSTEM 14-562

An Effective Approach to Equivalence the Blood Circulatory System through Human Heart with the Artificial Neural Network Nirmalya Chandra

139-143

AIJRSTEM 14-563

Estimation of radiation dose due to uranium in water to the public in Chamarajanagar district, Karnataka State, India K.M. Nagaraju, M.S. Chandrashekara, K. S. Pruthvi Rani and L. Paramesh

144-147

AIJRSTEM 14-565

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Optimization of Configuration of Inertial Propulsion System for Future Space Application Anand G 1, Jobin Joy 2, K Vijayan 3 Department of Mechanical Engineering, 3EFM/MDP 1,2 Sree Buddha College of Engineering, 3ISRO Inertial Systems Unit 1,2 Pattoor P.O., Alappuzha, Kerala, PIN: 690502, 3Thiruvananthapuram INDIA 1,2

Abstract: Conversion of centrifugal force to linear force is the basic principle of an inertial propulsion system. This thesis work describes an inertial propulsion system for developing a unilateral self- contained propulsion force in a predetermined direction using the effort of rotational-coupled mass motion. The system finds its applications in space transportation industry and satellite maneuvering. An attempt to study various mechanisms of inertial propulsion systems is made. Horizontal and vertical component of forces have been computed for one to five mass systems. The forces in both directions of different mass systems can be calculated with the aid of miscellaneous curves method (Inferior epitrochoid). The force patterns of corresponding mass systems are also plotted. The net propulsion force which is obtained in positive direction is in case of three mass systems and is selected for the configuration design. The force required for the application is in the vertical direction and hence the force in the radial direction has to be cancelled. Keywords: centrifugal force; linear force; space transportation industry; satellite maneuvering; mechanisms; inferior epitrochoid; three mass system; radial direction I. Introduction â&#x20AC;&#x153;Inertial propulsionâ&#x20AC;? (IP) is a novel principle producing movement in one direction using force from rotating inertial masses, which is physically equivalent to centrifugal force. Centrifugal force or thrust has not been used heretofore to produce directed motion in a predetermined direction. The centrifugal force produced by a body depends on the length of the radius connecting the axis of revolution to the rotating body. If the length of radius is increased in the desired direction of centrifugal thrust and then decreased in the opposite direction during rotation, an excess of force or thrust will be attained. This excess centrifugal force is exerted in the direction of increased radii. A greater excess force is produced by increasing the mass weight, and/or increasing the rpm and the length of the radii in the desired thrust direction at the expense of the length of radii in the opposite direction. In accordance with this invention, the length of the radius connected to the rotating mass or body, which is centrifugally driven by a power source, is varied by rotating the radius member through a revolving axis of rotation, the axis being off centered with respect to the rotating mass. Thus by having a series of off centered rotating masses laterally separated from each other and by predetermined angles between them at a particular instant of time but in separate planes of rotation, the rotating masses produce a summation of directed thrust or torque in the desired direction. An Inertial propulsion engine is a purely electromechanical system which does not burn any propellant. So this system is the source of clean energy which can be used for propulsion application. It can find applications in space transportation industry and satellite maneuvering. Satellite maneuvering during space missions is generally done with the help of propellant .With the introduction of inertial propulsion, the operation time may be extended further. This research work describes the working of an IPE configuration with a simple principle for the trajectory control of inertial masses so as to generate the unidirectional force for propulsion. This thesis work proposes a model with its principle, sub system details and challenges. II. Objectives The project objective is to study and analyse different inertial propulsion systems and to converge to an optimize configuration inorder to substitute conventional satellite maneuvering techniques and thus improve the life cycle. III. Problem Definition The operational life of the satellite is determined by the amount of propellant carried and the rate at which the propellant is used up. Propellant is used mainly for two purposes in case of satellite maneuvering. One is to maintain the satellite in its prescribed orbit and the other is to control rotation. Rotational operation, such as turning to a point in a new direction, is usually performed by angular momentum storage devices such as reaction wheels . It is generally preferable to use these devices instead of traditional thrusters, as they are powered by renewable electricity instead of propellant. Therefore, the less propellant that is be used for

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controlling rotation, the more life can be maintained. So inertial propulsion system is introduced for maintaining orbit with the use of renewable electricity instead of propellant. IV. Principle of Inertial Propulsion In an internal combustion engine, due to the fuel burning, linear impulse is created and the same is converted into angular momentum. A converse analogy of this process works in an inertial propulsion engine. It converts the angular momentum into linear momentum. The principal object of the present invention is to provide a machine or device operating as a variable radii centrifugal mechanism to produce torque or thrust in a prechosen direction. The invention is useful for producing oscillation or vibratory movement, for example, such as required for actuating agitators, shakers and the like equipment. Figure 1 Illustration of principle

A pair of counter rotating planetary gears fitted with eccentric masses around a sun gear is positioned in a plane. The sun gear does not rotate. During working, both units are rotated in opposite direction at same speed. Due to this movement of the masses, traction is generated in each and every point. The net traction force acting in the system would be the propulsion force generated by the device. Due to rotation, horizontal and vertical forces are generated in the system. The horizontal forces are balanced with each other with the introduction of two identical systems operated in opposite direction in same plane, where as we constantly get a vertical component of force. V. Configuration of Inertial Propulsion System The configuration of inertial propulsion system utilizes centrifugal force for propulsion. It contains series of offcenter rotating masses timed to take advantage of the positive centrifugal force. The configuration of different mass systems is shown in figure 2 to 6. That is from one mass system to five mass systems. Each configuration is selected for configuration design and force analysis for finding which system produces the positive propulsion force. In the case of configuration design, all gears used are selected with same module, pitch diameter and number of teeth for the purpose of getting the desired propulsion force. Figure 2 one, two, three, four, five mass systems

The configuration of one mass system in which the eccentric mass position clearly understands from the figure. The gear without mass is fixed and the gear with eccentric mass is rotating. An eccentric mass is attached on the rotating gear for producing the propulsion force in the desired direction. The configuration design of two mass systems consists of arrangement of two gears 1800 apart. The force which is required is in the upward direction so the arrangement of eccentric mass in the bottom rotating gear is always in the inner side. So the radius from the center of the fixed gear and the mass position is less and as a result net propulsion force is getting in the upward direction. The configuration design of three mass systems consists of arrangement of three gears 1200 apart. The configuration design of four mass systems consists of arrangement of four epicyclical gears with eccentric masses at 900 apart. The configuration design of five mass systems consists of arrangement of five epicyclical gears with eccentric masses at 720 apart. VI. Force Analysis of Inertial Propulsion System The force analysis of the configuration of inertial propulsion system can be done for finding the force generated by different mass system. Force generated both in horizontal (“X‟) and vertical (“Y”) direction of different mass system is calculated.Net force of different mass system is to be calculated and tabulated. Force pattern can be plotted in both directions of net force. The net propulsion force required is in the “Y‟ direction and the force in “X‟ direction is not required and is to be cancelled. Propulsion force analysis can be done with aid of graphical method. Mass path trajectory of single, two, three, four and five mass systems can be done with the aid of Miscellaneous Curve (Inferior epitrochoid) method. Propulsion forces of different mass systems in 30˚ increments can be calculated using the mathematical formula shown below.

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Also plotted force pattern in “x” and “y” direction for different mass systems and select the suitable mass system for the design of inertial propulsion system. The constant values used for finding the net propulsion forces are m = 250g = 0.25 kg, N=500 rpm. F = mrω² (1) Where, F= Propulsion force in Newton’s m = Mass of inertia disc in kg r = distance of inertia disc from the center of fixed gear in meters ω = Angular speed in rad/sec ɵ = Angular position from prime axis (center axis) in degrees A. Single Mass System Figure 3 Distance of eccentric mass

Figure 4 Angular position values

The mass path trajectory of single mass system is traced using inferior epitrochoid method is shown in figure 3 and 4. The path of the mass is traced for finding the radius which is the distance from the center of the fixed gear to the eccentric mass. Angle of mass from the horizontal or vertical axis of the fixed gear is also required for finding the propulsion force. The net propulsion force is shown in table I and force patterns are shown in figure 5 & 6. Table I Shows net propulsion force Sl.No 1 2 3 4 5 6 7 8 9 10 11 12 13

Net propulsion force [Fx (N)] 0 42.89 63.51 54.69 31.7 11.66 0 -11.66 -31.7 -54.69 -63.51 -43.02 0

Figure 5 Horizontal force vs mass angle

Net propulsion force [Fy (N)] 73.33 56.92 18.21 -18.83 -36.46 -38.14 -36.32 -38.14 -36.46 -18.83 18.21 57.09 73.33

Figure 6 Vertical force vs mass angle

B. Two Mass System The mass path trajectory of two mass systems uses the same figure 3 & 4 for finding the net propulsion force. In case of two mass systems the second mass position is starting on the seventh point of the first mass system. The table II shows the net propulsion force and force patterns are shown in figure 7 & 8. Table II Shows net propulsion force Sl.No

Net propulsion force [Fx (N)]

Net propulsion force [Fy (N)]

1 2 3 4 5 6 7 8 9 10 11 12 13

0 9.24 31.81 0 -31.81 -31.36 0 31.36 31.81 0 -31.81 -31.36 -31.36

37.01 30.22 -18.25 -37.66 -18.25 18.95 37.01 18.95 -18.25 -37.66 -18.25 18.95 18.95

Figure 7 Horizontal force vs mass angle

Figure 8 Vertical force vs mass angle

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C. Three Mass System Figure 9 Distance of eccentric mass

Table IIII Shows net propulsion force Sl.No

Net propulsion force [Fx (N)]

Net propulsion force [Fy (N)]

1 2 3 4 5 6 7 8 9 10 11 12 13

0 15.82 16.08 0.01 -15.83 -16.27 0 16.27 15.83 -0.01 -16.08 -15.82 -15.82

37.01 27.92 8.99 0 9.43 27.66 37 27.66 9.43 0 8.99 27.92 27.92

Figure 10 Angular position values

Figure 11 Horizontal force vs mass angle

Figure 12 Vertical force vs mass angle

The mass path trajectory of three mass systems is shown in figure 9 & 10. In case of three mass systems the three masses are positioned 1200 apart. The table III shows the net propulsion force and force patterns are shown in figure 11 & 12. D. Four Mass System Figure 13 Distance of eccentric mass

Figure 14 Angular position values

Table IV Shows net propulsion force

Figure 15 Horizontal force vs mass angle

Sl.No

Net propulsion force [Fx (N)]

Net propulsion force [Fy (N)]

1 2 3 4 5 6 7 8 9 10 11 12 13

0 31.36 31.81 0 -31.81 -31.36 0 31.36 31.81 0 -31.81 -31.36 0

37.01 18.95 -18.25 0 -18.25 18.95 37.01 18.95 -18.25 0 -18.25 18.95 37.01

Figure 16 Vertical force vs mass angle

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The mass path trajectory of four mass systems is shown in figure 13 and 14. In case of four mass systems the masses are positioned 900 apart. The tables IV show the net propulsion force. The force patterns are shown in figure 15 & 16. E. Five Mass System Figure 17 Distance of eccentric mass

Figure 18 Angular position values

Table V Shows net propulsion force

Figure 19 Horizontal force vs mass angle

Sl.No

Net propulsion force [Fx (N)]

Net propulsion force [Fy (N)]

1 2 3 4 5 6 7 8 9 10 11 12 13

-35.02 24.36 59.54 35.06 -24.12 -59.42 -35.29 24.25 59.76 35.41 -24.37 -59.64 -35.02

48.72 54.66 51.4 -48.4 -54.73 56.36 48.41 54.43 6.17 -48.45 -54.75 49.79 48.72

Figure 20 Vertical force vs mass angle

The mass path trajectory of five mass systems is shown in figure 17 & 18. In this case the masses are positioned 720 apart. The tables V show the net propulsion force. The force patterns are shown in figure 19 & 20. F. Assesment of propulsion force analysis From the propulsion force analysis the net propulsion force which is obtained in the upward direction is in case of three mass systems. But the radial force which exists is not applicable. So the radial force wants to be cancelled. G. Radial force cancellation in three mass system In case of inertial propulsion system the required force is only the vertical force. But the result obtained from the propulsion force calculation there is both horizontal and vertical force. For cancelling the horizontal force and adding up the vertical force, we required the same unit which is placed on the same plane and rotates in the opposite direction [From the result of the propulsion force the positive upward force i.e. vertical force is calculated in three mass system]. So the cancellation of radial force is done on this three mass system. Calculations of propulsion force based on the rotations; clockwise and anticlockwise is done from the figure 9 & 10. The tables VI & VII show net propulsion force. The force pattern in both directions is also shown in fig 21 & 22. Horizontal forces cancel each other results as the net force in horizontal direction is zero. Table VI Shows net propulsion force (clockwise)

Table VII Shows net propulsion force (counter-clockwise)

Sl.No

Net propulsion force [Fx (N)]

Net propulsion force [Fy (N)]

Sl.No

Net propulsion force [Fx (N)]

Net propulsion force [Fy (N)]

1 2 3 4 5 6 7 8 9 10 11 12 13

0 15.82 16.08 0.01 -15.83 -16.27 0 16.27 15.83 -0.01 -16.08 -15.82 -15.82

37.01 27.92 8.99 0 9.43 27.66 37 27.66 9.43 0 8.99 27.92 27.92

1 2 3 4 5 6 7 8 9 10 11 12 13

0 -15.82 -16.08 -0.01 15.83 16.27 0 -16.27 -15.83 0.01 16.08 15.82 15.82

37.01 27.92 8.99 0 9.43 27.66 37 27.66 9.43 0 8.99 27.92 27.92

Figure 21 Variation of horizontal force vs mass angle

Figure 22 Variation of vertical force vs mass angle

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VII. Conclusion From the result obtained from the calculation of propulsion force of different mass systems, the net propulsion force which is obtained is positive in case of three mass systems. So the configuration of three mass systems is applicable to the design. The force required for the application is only upward force and hence the horizontal force which exists in the system has to be cancelled. So for this purpose another unit is introduced on the same plane and rotates in the opposite direction. The result obtained is that the radial force is cancelled and the upward force is adding twice. From the analysis, a three mass configuration is selected in which two units are placed in a single plane. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

Novak, L.J.Centrifugal mechanical drive, US patent #3810, 394, Issued May 15, 1974 Foster,RE â&#x20AC;&#x201C; converting rotary motion in to Unidirectional motions ,US patent #3,653,269, issued April 4, 1972 Kellongg,MD , Gyroscopic inertial space drive ,US patent #3203,644,issued Aug.31,1965 Faral, A W. Inertial propulsion device, US patent #3,266,233,issued Aug.15,1966 Dean,N.L system for Converting rotary motion in to a unidirectional motion, US patent #2886,976,Issued May,19,1959 Thornson, Brandon.1990-businessplan available from fortune ventures, 118 Emerald Grove,Winnipeg,Manitoba,Canada,R3J1H2 Thornson, Branndon. Apparatus for developing propulsion force. US patent 4,631,971 Dec 30 1986 Inertial propulsion: Concept and Experiment, Part1 -Thomas Valone IECEC-93-Proceedings of the 28th Inter society Energy conversion Engineering Conference, Atlanta, Georgia Inertial propulsion: Concept and Experiment, Part 11-Thomas Valone AIAA-94-4167 I.B Laskowitz, Centrifugal variable thrust mechanism, US patent #2009, 780, Issued July 30, 1935 Laszio B Matyas, Propulsion apparatus, US patent #3584, 515, Issued June 15, 1971

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DENOISING FRAMEWORK USING SWITCHIG BILATERAL FILTER 1

Srinevasan.M, 2Balajee.B 1,2 Assistant Professor 1 Nandha Engineering College, 2Al-Ameen Engineering College, Erode – 638052, Tamil Nadu, India Abstract: To remove the noise in image, denoising filters are used. This noise removal process consists of two steps i.e. detection followed by filtering. Detection and filtering of noise is based on various noise detection algorithm and decision rules. Hence it provides high peak to noise ratio (PSNR) by efficiently removing the impulse/Gaussian mixed noise from image. In Proposed system, it uses switching bilateral filter for detect and remove impulse/Gaussian mixed noise from image. This method detects the noise and set the no. of iteration value automatically. The noise filtering is done by switching bilateral filter. Hence the merits are it needs less time complexity, high PSNR value, avoids the image degradation which is caused by too many no. of iteration and it’s suitable for colour as well as gray scale image. Index Terms: Noise detection, Image Noise, SQMV, Switching Bilateral Filter. I. Introduction In this project the image denoising process is effectively done by switching bilateral filter. This filter is used to identify the different types of noises in digital image processing. A bilateral filter is an edge-preserving and noise reducing smoothing filter. The intensity value at each pixel in an image is replaced by a weighted average of intensity values from nearby pixels. This weight is based on a Gaussian distribution. Crucially the weights depend not only on Euclidean distance but also on the radiometric differences (differences in the range, e.g. color intensity or Z distance). This preserves sharp edges by systematically looping through each pixel and adjusting weights to the adjacent pixels accordingly. We propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters. II. Methodology Switching Bilateral Filter with a Texture and Noise Detector for Universal Noise Removal Most often perform image processing operation is noise removal, while the enhancement of images. There are many noise and edge detectors with specific algorithm are available for removing noise. Detection are based upon robust Estimators and followed by filtering. Impulse detection and classification rate, shows a good performance in identifying noise even in mixed noise models. The switching scheme, noise detector to identify noisy pixels and differentiate the noisy pixels from noise free pixels. NOISE DETECTION MECHANISM

NOISY IMAGE

NL MEANS ALGORITHM FOR IMPULSE NOISE

ROR ALGORITHM FOR IMPULSE NOISE

FINAL RESTORED IMAGE

Figure 1: Block Diagram of Noise Removal

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In this paper we propose universal noise removal filter and SQMV. This filter is used to remove mixed noise, salt and pepper with uniform noise and salt and pepper with Gaussian noise. Sorted quadrant median vector is used to recognize edge and texture. Difference between the current pixel and reference medians are calculated for detect noise. Detection of noise is completed in two stages. The reference median is choosing for noise detection in an image. If differences are large, the pixel is considered as noisy pixel. Alternatively, when the differences are small, the pixel is considered as noise free pixel. Non proper reference median would guide to incorrect detection. So we have to find proper reference median. In Proper reference median can be selected depend upon edge and texture information. The noise detector in addition decides whether a current pixel should be filtered through an SBF or whether it should bypass the SBF. Switching bilateral filter is divided into two kinds, one is SBF and another one is SBF. In B1, B2 denote switching control signal created by the noise detector. The restored image has the original pixels if noise detector and edge and texture detector identify the pixels as noise free. If the pixel is detect impulse noise, and then the pixel is processed by the SBF. If the pixel is does not recognized as an impulse noise, although the current pixel and the reference median differences is still larger than a threshold, after that pixel is identified as Gaussian noise and is filtered by SBF. III. Image Noise Image noise is random (not present in the object imaged) variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the sensor and circuitry of a scanner or digital camera. Image noise can also originate in film grain and in the unavoidable shot noise of an ideal photon detector. Image noise is an undesirable by-product of image capture that adds spurious and extraneous information. IV. SQMV Switching-based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference. This reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter. In this paper, we propose a novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robust- ness in combating noise density variations. The proposed NASM filter consists of two stages. A soft-switching noise-detection scheme is developed to classify each pixel to be uncorrupted pixel, isolated impulse noise, non isolated impulse noise or image object’s edge pixel. “No filtering” (or identity filter), standard median (SM) filter or our developed fuzzy weighted median (FWM) filter will then be employed according to the respective characteristic type identified. Experimental results show that our NASM filter impressively outperforms other techniques by achieving fairly close performance to that of ideal-switching median filter across a wide range of noise densities, ranging from 10% to 70%. V Switching Bilateral Filter In this section, we propose a new universal noise removal algorithm: the switching bilateral filter (SBF). Let be the current pixel, and let be the pixels in a window surrounding. SQMV TO DETECT EDGE & TEXTURE

NOISY IMAGE

NOISE DETECTOR

SBF

RESTORED IMAGE

Figure 2: Flow diagram of Switching Bilateral Filter It is difficult for a bilateral filter to remove impulse noise because the difference between the noisy pixel and its neighbors is huge. This makes the radiometric weighting function too small to change the noisy pixel. The trilateral filter adds a new weighting function which is based upon ROAD statistic to remove the impulse noise. However, the trilateral filter has to be implemented iteratively. ws,j = exp – (1-Xi+Sj+t)2 / 2σ2r For an image with impulse noise, the trilateral filter processes each pixel individually and it takes too much processing time and creates a blurred result. By replacing with SQMR of the window in a bilateral filter, we can remove impulse noise without adding another weighting function. Additive Gaussian noise is characterized by adding to each image pixel a value with a zero-mean Gaussian distribution, and it affects all pixels of the image. Such noise is usually introduced during image acquisition. The zero-mean distribution property allows such noise to be removed by average pixel values locally. This relatively new class of denoising methods originates from the NL-means.Ui,j will be

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Un-like previous denoising methods, which were developed under local regularity assumption, the NL-means exploits the correlation in the entire image. Basically, the NL-means filter estimates a noise-free intensity as a weighted average of all pixel intensities in the image, and the weights are proportional to the similarity between the local neighborhood of the pixel being processed and the local neighborhoods of the surrounding pixels. Therefore, these nonlocal methods are very powerful of pre-serving image details when denoising. Figure 2 shows denoising algorithm for removing the impulse/Gaussian mixed noise from the image. The detection operation is done by the help of ROR value and for filtering operation ROR is extended to NLM means to remove the Impulse/Gaussian mixed noise. VI. Result & Analysis The switching bilateral filter removes the noise from the image very accurately without affecting the quality of the image.

Figure 3: shows the relationship between the PSNR, Time and image area.

Figure 4: Input image Image

30%

40%

50%

Lena

32.2007

33.2001

34.4035

Bridge

23.0002

24.3411

25.4309

Penguin

27.5032

29.7600

31.7809

Table I: Time complexity Analysis

Figure 5: Noisy input image and Denoised Output Image

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Figure 5 shows the noisy input content and denoised image with edge detection mechanism. Reference [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14].

Bo Xiong and Zhouping Yin ”A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means” IEEE transactions on image processing, vol. 21, no. 4, april 2012. Dabov.K, Foi.A, Katkovnik.V, and Egiazarian.K, “Image denoising by sparse 3-D transform-domain collaborative filtering,”IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, Aug. 2007 Elad.M and Aharon.M, “Image denoising via sparse and redundant representations over learned dictionaries,”IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736–3745, Dec. 2006. Buades.A, Coll.B, and Morel.J.M, “A review of image denoising algorithms, with a new one,”Multiscale Model. Simulink., vol. 4, no. 2, pp. 490–530, 2005. Buades.A, Coll.B, and Morel.J.M, “A non-local algorithm for image denoising,” inProc. Int. Conf. Comput. Vis. Pattern Recognit., 2005, pp. 60–65. Chen.T and Wu.H.R, “Space variant median filters for the restora-tion of impulse noise corrupted images,”IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., vol. 48, no. 8, pp. 784–789, Aug. 2001. Chen.T and Wu.H.R, “Adaptive impulse detection using center-weighted median filters,” IEEE Signal Process. Lett., vol.8, no. 1, pp. 1–3, Jun. 2001. Sun.T and Neuvo.Y, “Detail-preserving median based filters in image processing,”Pattern Recognit. Lett., vol. 15, no. 4, pp. 341–347, Apr. 1994. Pitas.I and Venetsanopoulos.A.N, “Order statistics in digital image processing,”Proc. IEEE, vol. 8, no. 12, pp. 1893–1921, Dec. 1992. Ko.S.J and Lee.Y.H, “Center weighted median filters and their ap-plications to image enhancement,”IEEE Trans. Circuits Syst., vol. 38, no. 9, pp. 984–993, Sep. 1991. Brownrigg.D, “The weighted median filter,”Commun. ACM, vol. 27, no. 8, pp. 807–818, Aug. 1984. Huang.T.S, Yang.G.J, and Tang.G.Y, “A fast two-dimensional me-dian filtering algorithm,”IEEE Trans. Acoust. Speech Signal Process., vol. ASSP-27, no. 1, pp. 13–18, Feb. 1979. Nieminen.A, Heinonen.P, and Neuvo.Y, “A new class of detail-pre-serving filters for image processing,”IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-9, no. 1, pp. 74–90, Jan. 1987. Coyle.E.J, Lin.J.H, and Gabbouj.M, “Optimal stack filtering and the estimation and structural approaches to image processing,” IEEE Trans. Acoust., Speech Signal Process., vol. 37, no. 12, pp. 2037–2066, Dec. 1989.

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LAND RECLAMATION USING PREFABRICATED VERTICAL DRAINS (PVD) IN PORT OF MOMBASA. Fred M. Machine Jomo Kenyatta University of Agriculture and Technology Department of Sustainable Materials Research and Technological Centre P.O Box 3909, 00200 Nairobi KENYA Kiptanui Arap Too Jomo Kenyatta University of Agriculture and Technology Department of Civil, Construction & Environmental Engineering Geotechnical & Transport Division P.O Box 62000, 00200 Nairobi KENYA Abstract: Land reclamation is used to describe two different activities. In the first sense, it involves modifying wetlands or waterways to convert them into usable land, usually for the purpose of development. It can also be a process in which damaged land is restored to its natural state. In both cases, the term is used to refer to some sort of process that is designed to fundamentally alter the characteristics of a piece of land to achieve a desired end goal. The need for additional port capacity in Kenya has rapidly risen over the last few years due to globalization. Due to high cost of land procurement, land reclamation has become a very promising alternative for expanding and constructing new ports. The presence of soft marine clay poses a major challenge for port development as it requires ground improvement. The Mombasa Port Development Project in the Republic of Kenya involved the filling of approximately 6 million cubic metres of sand for the land reclamation of a total area of about 49 hectares. Land reclamation was carried out using fill materials obtained from dredging granular material from the seabed at the borrow source situated near Tiwi in Kwale County. The ground improvement technique that involved combination of prefabricated vertical drain (PVD) with preloading was successfully applied in this project to improve the underlying compressible soils. The project comprises the installation of prefabricated vertical drains and the subsequent placement of surcharge to accelerate the consolidation of the underlying marine clay. The objective of the research is to assess the disparity of performance of ground improvement and to validate the performance of the prefabricated vertical drain system. Several geotechnical instruments were installed to monitor the degree of consolidation at both areas with PVD and areas without PVD as control area. The area subjected to the PVD showed that an average soil settlement of about 2.5m while the area without PVD had an average settlement of 0.5m during the study period. With respect to the soil formation at the project site in Mombasa, the degree of consolidation when using the PVD for ground improvement is higher than the area without PVD by 60%. This paper provides a case study of the ground improvement works carried out with prefabricated vertical drains at the Mombasa Port Development Project and confirmed it is the preferred option. Key Words: Land reclamation, Prefabricated Vertical Drains, Field Instrumentation, Ground Improvement and Preloading. I. INTRODUCTION From June 2012 till May 2013, the Mombasa Port Development Project (MPDP) in the Republic of Kenya involved the filling of approximately 6 million cubic metres of sand for the reclamation of a total land area of about 49 hectares. The land reclamation works were carried out in one phase. Land reclamation was carried out using fill materials obtained from dredging granular material from the seabed at the borrow source. The combination of prefabricated vertical drain (PVD) with preloading ground improvement technique was successfully applied in this project to improve the underlying compressible soils. The project comprises the installation of prefabricated vertical drains and the subsequent placement of surcharge to accelerate the consolidation of the underlying marine clay. In the entire project, a total of 5.367 million linear meters of vertical drains were installed making it the largest project in Kenya in which prefabricated vertical drains were used. In order to monitor the performance of ground improvement and to validate the efficiency of the prefabricated vertical drain system several geotechnical instruments were installed to monitor the degree of consolidation at both area with PVD and area without PVD as control area. Settlement gauges including deep settlement gauges were installed at the top of each sub layers

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whereas piezometers were installed at the center of each compressible sub layer in order to monitor the settlement and pore pressure dissipation. Settlement and pore pressure were monitored with close interval in the first three months and wider interval at the later part of monitoring. Ultimate settlements were predicted using the field settlement results applying the Asaoka and hyperbolic methods. A. Objectives Of The Study The main objective of this research is to compare the effect of using PVD and without use of PVD on ground improvement works carried out at Mombasa Port Development Project (MPDP). B. The Specific Objectives Specific objectives of the study is to establish the difference in degree of consolidation when using PVD and when not using PVD. II. MATERIALS AND METHODS A. Prefabricated Vertical Drains Historically the design of structures on soft compressible soils (clays) has created problems for civil engineers. Construction without some sort of soil treatment is usually impractical due to unpredictable long-term settlement. Although surcharging increases water pore pressure, settlement can take considerable time, often years, as the water lacks an easy path to leave the soil. Consolidation of soft cohesive soils using prefabricated vertical drains (also called wick drains or band drains) can reduce settlement times from years to months. Most settlement can occur during construction, thus keeping post-construction settlement to a minimum. Consolidation of a compressible soil occurs as pore water is squeezed from the soil matrix. The time for consolidation depends upon the square of the distance the water must travel to exit the soil. The installation of prefabricated vertical drains provides shortened drainage paths for the water to exit the soil. Larger prefabricated drains called strip drains are used for horizontal water removal at the surface replacing the previously used sand blanket. The strip drains are less expensive, install more easily and quickly, and provide better drainage.

Figure 1: Vertical Drain (PVD) Installation with Horizontal Strip Drain

B. Geotechnical Field Instrumentation Geotechnical instrumentation is the only means available of providing continuous records of the ground behavior from the point of instruments installation. Without a proper geotechnical instrumentation method or program, it would be difficult to monitor at any point of time the current degree of improvement of the soil. By analyzing the instrument monitoring results, it is possible to determine the degree of consolidation of the foundation soil before allowing the removal of the surcharge load and it is possible to ascertain the achievement of required effective stress and to indicate the necessity for remedial action. In order to study the performance of compressible soils under reclaimed fill, geotechnical instruments have to be installed. Various geotechnical field instruments were installed in instrumentation clusters to enable the instruments functions to complement each other. All instruments found in the instrument clusters were also extended and protected throughout the surcharge placement operations. In coastal land reclamation projects, instruments were installed either offshore prior to reclamation or onland after reclamation to the vertical drain installation platform level. Field instruments suitable for the study of consolidation behavior of underlying soils and monitoring of land reclamation works included surface settlement plates, deep settlement gauges, multi-level settlement gauges, liquid settlement gauges, pneumatic piezometers, electric piezometers, open-type piezometers, water standpipes, inclinometers, deep reference points and total earth pressure cells. A total of 75 geotechnical instruments (which includes 52 settlement plates, 9 inclinometers, 10 standpipes and 4 piezometers) were installed at the Mombasa Port Development Project (MPDP). Instrument monitoring was carried out at regular intervals so that the degree of improvement could be monitored and assessed throughout the period of the soil improvement works for the project. Instruments were monitored at close intervals of up to 2 times a week during sand filling and surcharge placement operations. C. Instrumentation Assessment Assessment of degree of consolidation could be carried out by means of field instrument monitoring at regular

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time intervals. Degree of improvement can be monitored and assessed throughout the period. Two simple instruments that can assess the degree of consolidation are settlement plates and piezometers. Details on assessment of degree of consolidation have been discussed by Bo et al. (1997) and Arulrajah et al. (2005, 2004a, 2004b). Degree of consolidation for settlement gauges can be computed based on the field settlement. Degree of consolidation is defined as percentage of magnitude of settlement that occurred at time t upon ultimate primary consolidation settlement as indicated in Equation 1. From measured field settlement and predicted ultimate settlement, degree of consolidation can be estimated. Ultimate settlement can be predicted for marine clays treated with vertical drains and preload by the Asaoka (Asaoka, 1978) or Hyperbolic (Tan, 1995) methods. Us (%)=St /Sα …………………………..(Equation 1) Where St = field settlement at any time t; Sα = ultimate settlement; and Us (%) = average degree of consolidation. Piezometers are utilized to measure the pore pressure in the soil. If regular monitoring is carried out to measure the piezometric head together with static water level, dissipation of excess pore pressure can be detected and thus degree of consolidation can be assessed. Average residual excess pore pressure is defined as ratio of excess pore pressure at time t upon initial excess pore pressure. Therefore degree of consolidation for a soil element, U u can be defined as shown in Eq. 2. Uu (%)=1-(Ut/Ui) …………………………..(Equation 2) Where Uu (%) = degree of consolidation for a soil element; Ut = the excess pore pressure at time t; and Ui = initial excess pore pressure which is equal to the additional load. III. RESULTS Mombasa's coastline is sheltered by a coral reef running parallel to the shore about one mile out from the highwater mark. Beaches of fine sand and gentle slope provide ideal sites for a rapidly developing hotel and cottage resort industry. Most of Mombasa sits on loose, sandy soil, but the eastern part stands on a porous coral base. The project site had three main soil formations as follows. (i) Cohesive soils (very soft marine clay) deposits formed from the surrounding shale erosion. Classified as CH (clay with high plasticity and the silt content is <10%). The N value was found to be 0 to 1. The shear strength was averaged to be 20-25 kN/m2. (ii) Weathered Shale (stiff clay and silt) also referred to as decomposed shale. Classified as CLML (clay with silt, silty clay) mostly 10-20% silt content. The N value was found to be above 30. The shear strength was averaged to be 26-30 kN/m2. (iii) Rock, which is highly, weathered shale with N value more than 50. The shear strength was averaged to be above 30 kN/m2. The Case Study Area consists of a PVD area at which vertical drains were installed at 1.2 meters spacing to an average depth of 25 meters, as well as an adjacent Control area where no vertical drains were installed. This enabled comparisons to be made between an area treated with vertical drains with the an untreated area. Both the areas were treated with the same height of surcharge preload. Instruments were installed and monitored at both the Vertical Drain Area and the Control Area. The instruments in the Control Area were installed prior to reclamation in off-shore instrument platforms. These instruments were protected as the reclamation filling works commenced in the area. Fig. 1 shows the geological profile of the Case Study Area and the typical details of onland and adjacent off-shore field instrumentation clusters. CONTROL AREA

VERTICAL DRAIN AREA

SURCHARGE

+9.4mCD +5.5m

RECLAMATION

-2mCD

MARINE CLAY -15mCD - 41mCD ROCK WEATHERED SHALE Fig. 1 Geological profile and details of field instrumentation at Case Study Area

Instruments in the PVD area were installed on-land at the vertical drain platform level of +4.5 m CD just

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before or soon after vertical drain installation at 1.2 meter square spacing. Surcharge was subsequently placed to +9.4 m CD. The analysis of the instrumentation results was carried out for both the PVD area and Control area after a monitoring period of about 10 months. Fig. 2 shows the construction sequence of works at the Case Study Area. The profile of the field instrumentation elevations at the Case Study Area has been recently described by Arulrajah et al. (2005, 2004a, 2004b).

Fig. 2 Construction sequence of the works at Case Study Area

A. Settlement Measurements Fig. 3 indicates the scales of settlements in the Prefabricated Vertical Drain (PVD) area. The deep settlement gauges that were installed in the different sub-layers indicate decreasing settlement with depth as would be expected. The PVD area indicated much greater settlement magnitudes as compared to the Control area. This indicates that the vertical drains are functioning. The settlement plates and the piezometer that were installed at the original seabed level gave similar reading for the magnitude and rate of settlement.

Fig. 3 Field settlement results at Prefabricated Vertical Drain Area

Fig. 4 compares the settlement plate results between the PVD area and Control area. The vast improvement of the PVD as compared to the Control Area is clearly evident in the figure 4 below. As expected the magnitude and rate of settlement of the PVD area is much higher than that of the Control area.

Fig 4 Comparison of field settlement at Case Study Area

B. Pore Pressure Measurements The piezometer monitoring data in the PVD area after correction of the piezometer tip settlement is shown in

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Fig. 5.

Fig 5 Excess pore pressure at Prefabricated Vertical Drain Area

Fig. 6 indicates the comparison of excess pore pressure isochrones between the PVD area and Control Area at various periods after surcharge placement. The rapid disappearance of excess pore water pressure with time is clearly evident in the PVD area. The slow rate of dissipation of excess pore water pressure with time is also noted at the Control area. It is evident that the degree of consolidation of the PVD area is far greater than that of the Control area.

Fig 6 Comparison of piezometer excess pore pressure isochrones

Fig. 6 above shows a considerable change in pore pressure for the PVD area between 6 months and 10 months after commencement of the study. While the pore pressure for the Control area is averagely constant during the same period. C. Degree of Consolidation The degree of consolidation was assessed from the settlement plates by the Asaoka (Asaoka 1978) and Hyperbolic (Sridharan & Sreepada 1981; Tan 1995) methods. The approaches of application of these methods for land reclamation projects on marine clay have been discussed by Arulrajah et al. (2004b) and Bo et al. (1997). Fig. 7 below compares the degree of consolidation as obtained from the settlement plates and piezometer results. Table 1 compares the degree of consolidation as obtained by the observational methods at the PVD area. It is seen that the methods give consistent results. The degree of consolidation of the piezometers was obtained from the isochrones of the piezometers. The degree of consolidation estimated from the pore pressure measurements is found to tie in well with that of the settlement plates at the PVD area, which is about 80%. The degree of consolidation estimated from the pore pressure measurements in the Control area is less than 20%.

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Fig 7 Comparison of degree of consolidation between PVD Area and Control Area Table 1 Degree of consolidated comparisons at Vertical Drain Area

Description Ultimate Settlement (m) Settlement to Date (m) Degree of Consolidation, U (%)

Asaoka 2.500 2.004 80.15

Hyperbolic 2.505 2.005 80.05

Piezometer 80.0

IV. DISCUSSIONS AND CONCLUSION The ultimate settlement predicted from the settlement plates by application of the Hyperbolic and Asaoka prediction methods was found to be about 2.5 meters. The assessment of degree of consolidation is found to be in good agreement for the Asaoka, Hyperbolic and piezometer methods. The settlement plates and piezometers indicate that the degree of consolidation of the PVD area had attained a degree of consolidation of about 80%. The piezometers indicate that the Control Area had only attained a degree of consolidation of about 20%. The instrumentation results in the Prefabricated Vertical Drain Area (PVD) indicates much higher degree of improvements as compared to the Control Area which indicates that the vertical drains are performing to improve the soil drainage system. ACKNOWLEDGEMENTS I take this opportunity to thank my Creator the Almighty God for His blessings and guidance always. Secondly I wish to express my profound gratitude and deep regards to my guide Eng. Takeshi Miyagawa and my other fellow consultants for his exemplary guidance, monitoring and constant encouragement throughout the course of the actual project. The help and guidance given by him time to time shall carry me a long way in the journey of my professional life. I also take this opportunity to express a deep sense of gratitude to Kenya Ports Authority Civil Engineering staff for their cordial support, valuable information and guidance, which helped me in completing my assigned tasks with ease. Lastly, I thank my Dad, my late Mum, my wife Jackie, my son Marita, daughter Masitsa, brothers and sister for their constant emotional support without which this assignment would not be possible. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9]

Arulrajah A (2005), “Field Measurements and Back- Analysis of Marine Clay Geotechnical Characteristics under Reclamation Fills”, PhD thesis, Curtin University of Technology, Perth, Australia. Arulrajah A, Nikraz H, Bo MW (2004a), “Observational Methods of Assessing Improvement of Marine Clay”, Ground Improvement 8(4): 151-169. Arulrajah A, Nikraz H, Bo MW (2004b) “Factors Affecting Field Instrumentation Assessment of Marine Clay Treated With Prefabricated Vertical Drains”, Geotextiles and Geomembranes 22(5): 415- 437. Asaoka A (1978), “Observational Procedure of Settlement Prediction. Soil and Foundations”, 18(4): 87-101. Bo MW, Chu J, Low BK, Choa V (2003), “Soil Improvement –Prefabricated Vertical Drain Techniques”, Thomson Learning. Singapore. Bo MW, Arulrajah A, Choa V (1997), “Assessment of degree of consolidation in soil improvement project”, Proceedings of the International Conference on Ground Improvement Techniques. Macau: 71-80 Bo MW, Arulrajah A, Choa V (1998), “Instrumentation and monitoring of soil improvement work in Land reclamation projects,” th 8 International IAEU Congress, Balkema, Rotterdam: 1333-1392 Sridharan A, Sreepada Rao A (1981), “Rectangular Hyperbola Fitting Method for One-dimensional Consolidation,” Geotechnical Testing Journal 4(4): 161-168 Tan SA (1995), “Validation of Hyperbolic Method for Settlement in Clays with Vertical Drains. Soil and Foundations 35(1),” 101-113

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Image Encryption using Chaotic Maps and DNA Addition Operation and Noise Effects on it 1,2

Manisha Raj1, Shelly Garg2 Indus Institute of Engineering and Technology, Kinana, Haryana, INDIA

Abstract: Encryption technology is an important measure to ensure the security of digital image, this paper's main purpose is to research how to encrypt images by chaos. In this paper, we propose a new digital image encryption scheme based on the linear chaotic map in order to meet the requirements of the secure encryption. The proposed scheme is described in detail, along with the security analyses including key space analysis, sensitivity analysis, and differential attack analysis. Firstly, we use the image encryption algorithm to convert original image to encrypted image. Now we apply noise on the encrypted image and then decrypt cipher image with noise back to original image. Also, noise has a little effect on original image and can easily be received by the receiver. The results show that the suggested image encryption scheme has some properties desirable in a good security cryptosystem. General Terms: Chaos theory, chaotic sequences, Correlation coefficient, Secret keys, Grey histogram, Image noise. Keywords: Image Encryption, Duffing Map, Gingerbreadman Map, Tinkerbell Map, DNA addition I. Introduction Since the computer networks have been widely applied, transmission for digital image over Internet has become more and more popular. However, due to the openness and sharing of networks, make the security of digital image has a serious threat in the process of transmission. Consequently, reliable security in storage and transmission of digital images is needed in many applications, including both public and private services such as medical imaging systems and military information systems. Chaotic system is a deterministic nonlinear system that has many important properties, such as aperiodic, sensitive dependence on initial conditions and system parameters, density of the set of all periodic points and topological transitivity, etc. It provides a new approach for cryptography and a large number of chaos-based image cryptosystems have been suggested and investigated during the past decade[4-8].It is neither periodic nor convergent, but significantly sensitive to its initial conditions ,so the information encryption technologies which adopt chaotic signals have a broad application future. At present, chaotic securer communication systems have been developed to certain, while extending the chaotic security communication systems to Internet and multimedia security is becoming a hot research spot. The basic idea of chaos-based image encryption scheme is implementation both position permutation and grayscale substitution on an image by using chaotic key stream and the security depends on the unpredictability of the pseudorandom key stream. The chaos-based image encryption scheme has the advantages of strong against known/chosen plaintext attack and fast encryption speed. Bimolecular computing has emerged as an interdisciplinary field that draws together molecular biology, chemistry, computer science and mathematics. Our knowledge on DNA nanotechnology and bimolecular computing increases exponentially with every passing year. II. BASIC THEORY A. CHAOTIC MAP: In this paper we used thre chaotic maps: duffing map, tinkerbell map, gingerbreadman map. A.1 Duffing map The Duffing map (also called as 'Holmes map') is a discrete-time dynamical system. It is an example of a dynamical system that exhibits chaotic behavior. The Duffing map takes a point (xn, yn) in the plane and maps it to a new point given by (1) The map depends on the two constants a and b. These are usually set to a = 2.75 and b = 0.2 to produce chaotic behaviour. It is a discrete version of the Duffing equation.

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A.2 Tinkerbell Map The Tinkerbell map is a discrete-time dynamical system given by: (2)

Some commonly used values of a, b, c, and d are

Like all chaotic maps, the Tinkerbell Map has also been shown to have periods; after a certain number of mapping iterations any given point shown in the map to the right will find itself once again at its starting location. A.3 Gingerbreadman map In dynamical systems theory, the Gingerbreadman map is a chaotic 2D map. It is given by the transformation:

(3) B. Scheme for Encryption and Decryption of Image B.1 DNA encoding and decoding: The information in DNA is stored as a code made up of four nucleic acid bases: adenine (A), guanine (G), cytosine (C), and thymine (T). DNA bases pair up with each other, A with T and C with G, to form units called base pairs, which are complement to each other. As in the binary mathematics 0 and 1 are complement, so 00 and 11 are complement, similarly 01 and 10 are complement. In this paper we use 00=A, 01=C, 10=G and 11=T. In the 8 bit grey images each pixel is given by a DNA sequence of length 4.For example: If the first pixel value of the original image is 1 convert it into a binary steam is [10101101] , by using DNA encoding rule to encode the stream, we can get a n sequence [TTGA]. Whereas use C, A, T, G to denote 00, 10, 11, respectively, and to decode above DNA sequence, can get a binary sequence [10101101]. B.2 Addition and subtraction algebraic operation for DNA sequences: Addition and subtraction operation for DNA sequences are performed according to traditional addition and subtraction in the Z2. For example, 11 + 10 = 01, 01-11 = 10.We use 00, 01, 10, 11 to denote A, C, G, T respectively. That is G+T=A, A-C=G…… The details of addition and subtraction rule are shown in Table 1 and Table 2 respectively. In other words, the addition algebraic operation table is a double helix structure which has satisfied the Watson-Crick complement regulation. Subtraction is the inverse operation of addition, but whose structure is not double helix structure. Table 1. Addition Operation Table 2. Subtraction Operation +

G T

T A

A C

C G

III. THE PROPOSED ENCRYPTION ALGORITHM In this section, we will study the procedure of the image encryption based on DNA sequence addition operation in detail. Firstly we have to produce the secret keys using the original image and secondly, divide the original image into blocks and add these blocks by using DNA sequence addition operation and carry out DNA sequence complement operation. At last, decode the scramble DNA matrix to binary matrix by using the rule of A = 00, T = 01, C = 10, G = 11, and then reconstruct it to a image matrix. A. Generation of secret sequences For the generation of the secret sequences, a input of 8 bit grey image A as the original image, A = A( aij), i = 1,2, ... , m, j = 1,2, . .. , n. Here, aij is the pixel value of image, (i, j) is the position of image, and (m, n) is the size of image. Using following formulas to calculate k1 and k2. (4) (5) Choose two initial values x1, y1 and four system control parameters µ1 , µ2 , µ3 , µ4.Now calculate x0 , y0 using the following pseudo code: x0 = x1 + k1 if x0 > 1 then

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x0 = mod(x0,1) else x0 = x0 end B. Algorithm Principle This algorithm can be divided into following steps: Step1: Convert the original image(m, n) matrix into binary matrix(m, nx8) then use DNA encoded rule to obtain a matrix K of size (m, nx4); Step 2: Divide K into small blocks called cells k{i,j}, i=1,2,…., m/4, j=1,2,…..,n. size of each cell is 4 x 4; Step 3: Generate two chaotic sequences X= { x0, x1, ….., xm/4}. Y= {y0, y1,…….,yn}, using chaotic maps and initial values x0,y0 and control parameters of equations; Step 4: Reconstruct X and Y to row matrix and column matrix respectively. Do multiply operation on X and Y, we get a matrix k’ then convert it to binary matrix using Eq (9). Using DNA encoded matrix rule we got a DNA encoded matrix k’. Divide matrix k’ into small cells k’{i,j} of size 4 x 4; Step 5: Add k{i,j} and k’{i,j} according to DNA addition rule shown previously to obtain added cells as B{i,j}; Step 6: Recombine these small cells B{i,j}, we will get a new matrix C; Step 7: Again two chaotic sequences Z1 and Z2 are generated whose lengths are m and nx4. Reconstruct Z1 and Z2 to two matrices Z1 (m, 1) and Z2 (1, nx4). Do multiply operation, we get Z matrix whose size is (m, nx4); Step 8: Map the value of Z into (0, 1) by mod (Z, 1). Gets binary sequence matrix using following threshold function: 0, 0  Z (i, j )  0.5 f ( x)   1, 0.5  Z (i, j )  1

(6) Step 9: If Z(i,j) = 1, C(i,j) is complemented, otherwise it is unchanged. Here, we get a complemented matrix P; Step 10: carry out inverse process of step 1 for matrix P, we will obtain the real matrix D. here D is our encrypted image; Decryption can be done by moving from step 10 to step 1, except addition operation is replaced by subtraction operation. Receiver obtains secret keys from sender. IV. EXPERIMENT RESULT In this paper we used standard 256 x 256 grey image Lena as the original image, use Matlab 7.0.1 to simulate experiment. Set x1 = 0.89, y1=0.29. In case of Duffing map, four secret keys x(0)=0.89 ,y(0)=0.29 ,a=2.75 ,b=0.2.Tinkerbell map has six secret keys x(1)= -0.72,y(1)= -0.64,a=0.9,b=0.6013,c=2,d=0.5.The Gingerbreadman map has 2 secret keys x1=.0.7298 and y1=0.7654.Figure 1(a) shows the original image, the encrypted image of Duffing, Tinkerbell, and Gingerbreadman map are shown in figure 1(b), (c), (d), respectively and figure 1(e) shows decrypted image. Here, Duffing map shows the best encryption effect on image.

(a)

(b)

(c)

(c) (d) Figure 1(a) the original image (b) Encrypted image (duffing map) (c) Tinkerbell map (d) Gingerbreadman map (e) Decrypted image

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V. THE SECURITY AND ANALYSIS A good encryption algorithm should resist all kinds of known attacks, such as exhaustive attack, statistical attack and differential attack, etc. In this section, we will discuss the security analysis on the proposed encryption scheme A. Ability of resisting exhaustive attack A.1 Key sensitivity analysis: Chaotic maps are highly sensitive to initial condition and system control parameters. If there is a minute change, then decrypted image will no longer be similar to original image. Some secret key tests are shown here. Figure 2 (a), (b), (c), shows the decrypted image and corresponding histogram with wrong secret key. Here the correct key is x(0)=0.7199 and the incorrect key is xâ&#x20AC;&#x2122;(0)=0.71990000000001. We can see that the histogram of the decrypted images are fairly uniform and the decrypted images are different from the original image. The sensitivity of the other parameters (secret keys) is also same, we have not shown it. Based on the above argument, our algorithms are sensitivity to the secret key which demonstrates it has ability of resisting exhaustive attack.

(a)

(b)

(c) Figure 2: Sensitivity to secret key (0.7199), Decrypted image with secret key (0.71990000000001) and corresponding histogram (a) Duffing map (b) Tinkerbell map (c) Gingerbreadman map B. Ability of resisting statistical attack B.1 Histogram analysis: Histogram shows the number of pixels for any grey value in the image. Considering the statistical analysis of the original image and the encrypted image. Figure 3 (a), (b), (c), and (d),show the grey-scale histograms of the original image and the encrypted images(of Duffing, Tinkerbell, and Gingerbreadman map) respectively. Comparing all the histograms we find that pixel grey values of the original image are concentrated on some values, but the histogram of the encrypted images is very uniform, and that the encrypted images transmitted do not provide any suspicion to the attacker, which can strongly resist statistical attacks. Grey histogram of Duffing map shows the best results.

(a)

(b)

(c)

(d)

Figure 3: The grey histogram of the original image and the encrypted image. (a) shows grey histogram of original image and (b), (c), (d), shows grey histogram of encrypted image of Duffing map, Tinkerbell map, Gingerbreadman maps respectively. B.2 Correlation coefficient analysis: In this section the horizontal, vertical and diagonal correlation coefficient of pixels studied To do this we choose 2000 pair of adjacent pixels (horizontal, vertical, and diagonal) from

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original image and encrypted image. To test the correlation between two adjacent pixels in plain image and encrypted image, we can use formula of correlation coefficient are:E ( x) =

–

D( x) =

–

COV (x,y) = =

–

Where x and y are grey value of two adjacent pixels in the image, cov(x, y) is covariance, D(x) is variance, E(x) is mean. Figure 4 (a), (b), (c), (d), and (e) shows the correlation of two horizontally adjacent pixels of original image and encrypted images (logistic, cross chaotic, ikeda, and henon) respectively. Here, we can see the correlation between the adjacent pixels is greatly reduced. Detailed analysis is shown in Table 3, 4, 5, and 6. From the results of Table 3 to 6, we find that the correlation coefficient of the adjacent pixels in encrypted image is very small. It can clearly be seen our algorithm can destroy the relativity effectively, the proposed image encryption algorithm has strong ability of resisting statistical attack. Table 3- Duffing Map Direction

Original image

Horizontal

Table 4- Tinkerbell Map Direction

Original image

0.9484

Encrypted image 0.0091

Horizontal

0.9484

Encrypted image -0.0065

Vertical

0.9910

0.0893

Vertical

0.9910

0.5910

Diagonal

0.8885

0.0055

Diagonal

0.8885

-0.0460

Table 5- Gingerbreadman Map Direction

Original image

Encrypted image

Horizontal

0.9484

0.4140

Vertical

0.9910

-0.1804

Diagonal

0.8885

-0.0329

Table 3, 4, 5 shows correlation coefficient of two adjacent pixels

(a)

(b)

(c)

Figure 4: Correlations of two vertically adjacent pixels in (a) original image and in encrypted images of (a) Duffing map, (b) Tinkerbell map, (c) Gingerbreadman maps C. Ability of resisting differential attack As we know, in order to avoid the known-plaintext attack and the chosen-plaintext attack, a good encryption algorithm should have the desirable property which spreads the influence of a single plaintext bits over as much of the ciphertext as possible so as to hide the statistical structure of the plaintext. This means the small difference of the plaintext should be diffused to the whole ciphertext. Attackers often make a slight change for the original image, and use the proposed algorithm to encrypt for the original image before and after changing, through comparing two encrypted image to find out the relationship between the original image and the

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encrypted image. It is called differential attack. NPCR and UACI are the two criterion that examine the effect of changing the just one pixel in the original image. The number of pixels change rate (NPCR) is the pixel change rate at the encrypted image for changing a pixel at the original image and unified average changing intensity (UACI) is the mean of these changes. These two measures are defined as:

Where E1 and E2 denote two encrypted images, respectively, W and H are the width and height of image, and the grayscale values of the pixels at grid (i, j) of E1 and E2 are labeled as E1(i, j) or E2(i, j) , respectively. The results are obtained by simulation for all four maps and are shown in Table 6. Table 6. Ability to resist Differential attack Duffing

Tinkerbell

Gingerbreadman

NPCR

99.9969

UACI

0.0021

0.0019

VI. CONCLUSION In this paper, we proposed a new image encryption algorithm based on DNA sequence addition. From above discussing, the pixel grey values of the original image are scrambled by DNA sequence addition operation and DNA complement operation completely. Through the experiment result and security analysis, we find that our algorithm has better encryption effect, larger secret key space and high sensitive to the secret key. We have compared four different chaotic maps: Duffing map,Tinkerbell map, Gingerbreadman map. Through the simulation results, histogram analysis and correlation analysis we have found out that duffing map showed best results than other three chaotic maps. It is sensitive to the secret keys, it has larger keyspace, and it gives best encrypted image. All these features show that our algorithm is very suitable for image encryption. We have also shown the effect of various noises on the image. Although the quality of image degrades due to the effect of noise but not to an extend that image cannot be recognized. VII. FUTURE SCOPE This paper mainly focused on comparison of chaotc maps and noise effects. Further research can be done on the following points:  Noise effects can be removed by using suitable noise filtering scheme.  Blurring effects can be deblurred from the image using suitable algorithms. REFERENCES [1] [2]

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Chen Wei, Zhangxin, “Image Encryption Algorithm Based on Henon Chaotic System”, 2009 IEEE Dong enzeng, Chen zengqiang, Yuan zhuzhi, Chen zaiping,A Chaotic Image Encryption Algorithm with The Key Mixing Proportion Factor, 2008 International Conference on Information Management, Innovation Management and Industrial Engineering, 2008,169-174. Hasan S. M. Al-Khaffaf, Abdullah Z. Talib, Rosalina Abdul Salam, “Removing Salt-and-Pepper Noise from Binary Images of Engineering Drawings”, IEEE 2008 International conference on DNA computing and molecular programming Feb, 2009 Jun Peng, Shangzhu Jin, Yongguo Liu etc., A Novel Scheme for image Encryption Based on Piecewise Linear Chaotic Map, Cybernetics and Intelligent Systems,2008, 1012-1016. Ling Wang, Quen Ye Yaoqiang, Yongxing zou , Bo Zang, “An Image Encryption Scheme based on cross chaotic map”, 2008 IEEE Peng Fei, Shui Sheng Qiu, Long Min, “An Image Encryption Algorithm based on Mixed Chaotic Dynamics Systems and external keys, 2005 IEEE” Rafael Gonzalez, Richard Woods, Steven Eddins, “Digital Image Processing using Matlab” Prentice Hall Publication 2003 Raymond H. Chan, Chung-Wa Ho, and Mila Nikolova, “Salt-and-Pepper Noise Removal by Median-type Noise Detectors and Detail-preserving Regularization”, July 30, 2004 Robart L. Devaney, A First Course in Chaotic Dynamical Systems, Perseus Books Publishing, L.L.C. Qiang Zhang, Ling Guo, Xianglian Xue, Xiaopeng Wei, An Image Encryption Algorithm Based onDNA Sequence Addition Operation, 2009 IEEE Qian Wang, Qiang Zhang, Changjun Zhou, “A Multilevel Image Encryption Algorithm Based on Chaos and DNA Coding”, 2009 IEEE. Xiaogang Jia, “Image Encryption Using Ikeda map”, 2010 International conference on intelligent computing and cognitive infomatics.

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American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

Shortest Path Problem under Rough and Uncertain Environment Sagarika Biswal1, S. P. Mohanty2 1,2 Department of Mathematics College of Engineering and Technology, Techno Campus, Ghatikia, Bhubaneswar, Odisha, India Abstract: Shortest path problem in a network where the weights of edges are imprecise, many authors have developed methods considering the weights as fuzzy numbers. Stochastic approach has also been used for the purpose. In this paper, shortest path problem under uncertain environment has been studied, where the edges are attached with rough weights. Two shortest paths; α-optimistic and α-pessimistic are obtained. As a compromise shortest path, uncertain linear and uncertain zigzag distributions are used to find two other shortest paths as L-shortest path and Z-shortest path. Uncertain reliability of path is introduced and most reliable shortest path by finding k-shortest paths of minimum number of edges is obtained with a compromise of the weight. Key words: Shortest path problem, k-shortest path, Rough variable, uncertain distribution, Rough set

1. Introduction The shortest path problem is one of the most fundamental problems in graph theory which has many applications in the diversified field like transportation engineering, communication engineering, computer science, operations research etc. In a network, the shortest path problem aims at finding the path from one source node to the destination node with minimum weight, where some weight is attached to each edge connecting a pair of nodes. In classical graph theory, the weights attached to different edges are supposed to be definite. Many algorithms have been developed to find the shortest path where the weights are definite and deterministic in nature. In many practical applications, the parameters determine the weights of the edges are not precise and uncertain due to lack of proper information. To deal the impreciseness of the weights, fuzzy numbers are used by many authors and different methods for finding fuzzy shortest path have been proposed. The ranking and comparison of fuzzy numbers can be done in varieties ways leading to different methods to solve fuzzy shortest path problem. The paper due to Dubois and Prade[1] is one of the first on this area where they have extended the classic Floyd and Ford-Moore-Bellman algorithms under fuzzy environment. Thereafter, authors like Klein[2] obtained the shortest path corresponding to the threshold of membership degree; Yagar[3 ] studied the problem in terms of possibilistic production system; Okada and Soper [4] introduced the concept of non-dominated path. Xiaoyu Ji et al [5] using the credibility measure proposed three concepts of fuzzy shortest path: expected shortest path, α-shortest path and the most shortest path and formulated three models for the fuzzy shortest path according to different decision criteria. Yong Deng et al [8] proposed generalised Dijkstra’s algorithm using graded mean integration representation of fuzzy numbers to find the shortest path. To deal with the uncertainties, probability theory has also been used to find the shortest path [6-7]. However, randomness is not the only type of uncertainty in the real world problem and sometimes, the probability distributions of the weights of the edges are difficult to acquire due to lack of historical data. In this paper, to tackle the issue of uncertain weights of the edges, rough variable is used where each edge is assigned a rough value as per the concept defined by Liu[12]. In the process, two shortest paths are obtained corresponding to α-pessimistic value and α-optimistic value of the corresponding rough weights. Further for a compromise solution, two other approaches are proposed, where linear and zigzag uncertain distributions are used. Eventually, four shortest paths are obtained where one can be selected considering the decision criteria used by the decision maker. Further, as the weights are uncertain in nature, reliability of the paths is also very important to study. We have defined uncertain reliability of the path and obtained the most reliable shortest path under uncertain weights by finding k-shortest paths having minimum number of edges. The rest of the paper is organised as follows: Section 2 gives a brief introduction and important definitions and concepts of the basic theory used in the proposed work. The problem description is given in section 3. Two methods are proposed to find compromise solutions in section 4. Reliability analysis of shortest paths is discussed in section 5. One numerical example is given in section 6 to illustrate the efficiency of the proposed method. Lastly, the conclusion is given in section 7.

Sagarika Biswal et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), JuneAugust, 2014, pp. 117-124

II. Preliminaries In this section, some basic concepts on rough set, uncertainty theory and rough variable related to the work are given. A. Rough set Rough set theory developed by Pawlak [8-9] is a mathematical tool for dealing with uncertain and incomplete data without any prior knowledge about the data. We deal only with the available information provided by the data to generate conclusion. Let U be a finite non empty set called the universal and let R be a binary relation defined on U. Let R be an equivalence relation and R(x) be the equivalence class of the relation which contains x. R shall be referred as indiscernibility relation. For any X ⊆ U, the lower and upper approximations of X are defined by R (X ) = { x ∈ U : R (x ) ⊆ X } , R (X ) = { x ∈ U : R (x ) ∩ X ≠ φ } The lower approximation R ( X ) is exact set contained in X so that the object in R (X ) are members of x with certainty on the basis of knowledge in R, where the objects in the upper approximation R (X ) can be classified as possible members of X. The difference between the upper and lower approximation of X will be called as Rboundary of X and is defined by B N R ( X ) = R (X ) − R ( X ) .

The set X is R-exact if B N R ( X ) = φ , otherwise the set is R- rough set. B. Uncertainty theory B. Liu [11], [12] has developed uncertainty theory which is considered as a new approach to deal with indeterminacy factors when there is a lack of observed data. In this section, some basic concepts of uncertainty theory has been reviewed which shall be used in this paper. B.1 Uncertainty measure Let L be a δ - algebra on a nonempty set Γ . A set function M : L → [0,1] is called an uncertain measure if it satisfies the following axioms Axiom 1: (Normality axiom) M ( Γ ) = 1 for the universal set Γ Axiom 2: (Duality axiom) M ( Λ ) + M ( Λ c ) = 1 for every event Λ Axiom 3: (sub additive axiom)

For every countable sequence of events   M U Λ i ≤  i= 1  ∞

The triplet ( Γ , L , Μ

)

∑

Λ1 , Λ 2 ,..... we have

(Λ i )

i=1

is called an uncertainty space.

(

Axiom 4: (Product measure) Let

∞

Γ k , Lk , Μ

) be uncertainty spaces for k = 1, 2……The product uncertain

measure is an uncertain measure satisfying M

where,

  

∞

∏

k = 1

  = 

∞

(Λ

)

k = 1

Λ k an arbitrary chosen events for Lk for k = 1,2…… respectively.

B.2 Uncertain variable An uncertain variable ξ is essentially a measurable function from an uncertainty space to the set of real numbers. Let

be an uncertain variable. Then the uncertainty distribution of φ ( x ) = M {ξ ≤ x } for any real number x.

is defined as

An uncertain variable ξ is called linear if it has linear uncertainty distribution L (a,b) φ

(x )

0  x − a  =   b − a  1

if x ≤

if a ≤

x ≤

if x > b

where, a and b are real numbers with a < b.

Figure 1. Linear uncertainty distribution

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An uncertain variable is said to be zigzag if it has distribution Z(a, b, c)

0  x−a   2(b − a ) φ (x ) =   x + c − 2b  2( c − b )  1

if x ≤ a if a ≤ x ≤ b if b ≤ x ≤ c if x > c

Figure 2. Zigzag uncertainty distribution An uncertain distribution

φ is said to be regular if its inverse function φ −1 (α ) exists and is unique for each

α ∈ ( 0,1) . The linear uncertainty distribution L(a, b) is regular and its inverse uncertainty distribution is φ − 1 (α ) = (1 − α ) a + (α ) b The zigzag uncertainty distribution Z(a, b, c) is also regular and its inverse uncertainty distribution is

if α < 0.5 (1 − 2α ) a + 2α b (2 − 2α )b + (2α − 1)c if α ≥ 0.5

φ − 1 (α ) = 

C. Rough variable The concept of rough variable is introduced by Liu[12]as uncertain variable. The following definitions are based on Liu[12]. Definition 1 : Let Λ be a non empty set, A be σ - algebra of subsets of Λ , ∆ be an element in A, and π be a non negative, real- valued, additive set function on A. The quadruple ( Λ , ∆ , A , π ) is called a rough space.

ξ on the rough space ( Λ , ∆ , A , π ) is a measurable function from Λ to the set of real numbers ℜ such that for every Borel set B of ℜ , we have {λ ∈ Λ | ξ ( λ ) ∈ B } ∈ A . Then the lower and upper approximation of the rough variable ξ are defined as follows Definition 3: Let ( Λ , ∆ , A , π ) be a rough space. Then the upper and lower trust of event A is defined by Definition 2: A rough variable

T r (A ) =

π {A } π {Λ }

and T r ( A ) =

π {A ∩ ∆ } π {Λ }

The trust of the event A is defined as T r (A

1 (T r ( A ) + T r ( A 2

))

The trust measure satisfies the followings: T r ( Λ ) = 1 , T r (φ ) = 0

T r ( A ) ≤ T r ( B ) w h ere A ⊆ B T r (A ) + T r (A c ) = 1 Definition.4: Let

ξ1 , ξ 2

be rough variables defined on the rough space

( Λ, ∆, A, π ) . Then their sum and

product are defined as

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(ξ 1 + ξ 2 )( λ ) = ξ 1 ( λ ) + ξ 2 ( λ ) (ξ 1 . ξ 2 )( λ ) = ξ 1 ( λ ) .ξ 2 ( λ ) Definition 5: Let ξ be rough variables defined on the rough space ( Λ, ∆, A, π ) and α ∈ (0,1] ξ su p (α ) = su p {r | T r {ξ ≥ r } ≥ α } is called α -optimistic value of ξ . ξ in f (α ) = in f {r | T r {ξ ≤ r } ≥ α } is called α -pessimistic value of ξ . Definition 6: Let

then

ξ be rough variables defined on the rough space ( Λ, ∆, A, π ) . The expected value of ξ is

defined by ∞

E (ξ

) = ∫ T r {ξ

≥ r}d r −

Definition 7: The trust density function

φ (x ) =

∫

T r {ξ ≤ r } d r

−∞

∫

f : R → [ 0 ,∞ )

of a rough variable

ξ is a function such that

f ( y ) dy holds for all x ∈ ( −∞, ∞ ) , where φ is trust distribution of ξ . If ξ = ([ a ,b ] , [ c , d ])

−∞

be a rough variable such that c

φ (x

)

φ ( x ) = Tr {ξ ≤ x}

≤ a < b ≤ d, then the trust distribution

0   x − c  2 (d − c )   [( b − a ) + ( d − c ) ] x + 2 a c − a d − b c =  2 ( b − a )( d − c )   x+d − 2c   2 (d − c )  1 

if x ≤ c if

c ≤ x ≤ a

if a ≤ x ≤ b if b ≤ x ≤ d if x ≥ d

And the trust density function is defined as 1   2 (d − c )   1 1 f (x ) =  +  2 (b − c ) 2 (d − c )  0  

if c ≤ x ≤ a o r b ≤ x ≤ d if a ≤ x ≤ b o th e rw is e

α-optimistic value to ξ = ([ a ,b ] , [ c , d ]) is   (1 − 2α ) d + 2α c ,   ξ sup (α ) =  2 (1 − α ) d + ( 2α − 1 ) c ,   d ( b − a ) + b ( d − c ) − 2α ( b − a )( d − c )  ,  (b − a ) + ( d − c )

if α ≤

d−b ; 2 (d − c )

if α ≥

2d − a − c ; 2 (d − c )

otherw ise.

α-pessimistic value to ξ = ([ a ,b ] , [ c , d ]) is

ξ in f

  (1 − 2α ) c + 2α d ,    (α ) =  2 (1 − α ) c + ( 2α − 1 ) d ,   c ( b − a ) + a ( d − c ) + 2α ( b − a )( d − c )  ,  (b − a ) + ( d − c )

if α ≤

a−c ; 2 (d − c )

if α ≥

b+ d − 2c ; 2 (d − c )

oth erw ise.

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The expected value of

ξ is

(ξ ) =

1 4

+ b + c + d

)

III. Problem description Let G = (V , E) be a directed graph, where V = {v1, v2, v3, ..., vn} be the set of nodes and E be the set of directed edges. Each edge is denoted by an ordered pair (i , j), where i, j ∈V . It is supposed that there is only one directed edge (i , j) from the node i to the node j. Let the vertex 1 be the source node and node t be the destination node. We define a path pij from the node i to node j as the sequence of alternate nodes and edges as { i, (i, i1), i1, (i1 , i2), i2, ...(ik-1 , ik), ik, (ik , j), j}. Let wij = ([aij , bij], [cij , dij]) be the rough number associated with the edge (i , j) as the uncertain weight of the edge. The weight may be the distance, time required to traverse though the edge, cost of travelling etc. Then the uncertain shortest path problem is to find one path from the source node 1 to destination node t such that the sum of weights of the edges in the path is minimum. When the weights attached to the edges are crisp, then the famous Dijkastra’s algorithm can be applied to find such a path. In this case, as the weights are rough number and comparison of weights as in case of crisp number is not possible. Therefore it is necessary to convert the rough number to its equivalent crisp number so that the said algorithm can be used to find the shortest path. The one possible technique is to compare the weights of the edge by considering the corresponding α-optimistic values and α-pessimistic values of the rough weights. Let αij and βij be the α-optimistic values and α-pessimistic values of the rough weights wij = ([aij , bij], [cij , dij]), where cij ≤ aij < bij ≤ dij , considering the trust level (level of confidence) as α in the interval (0 , 1] One shortest path can be obtained from the source node 1 to the destination node t considering the α-optimistic values αij, i, j =1,2,3 ...n and i≠ j which can be considered as optimistic shortest path. Similarly, another shortest path can be obtained using the α-pessimistic values βij, which can be considered as pessimistic shortest path. The two paths may coincide but the total weights of the arcs in the path shall be different; one corresponding to the optimistic values and the other corresponding to the pessimistic values. Here two problems shall arises: (i) If the paths are different, the decision maker always likes to find one path instead of choosing one from two possible paths, (ii) As the weights are uncertain in nature, the decision maker must be sure about the reliability of the path under uncertain environment. In the next two sections, we proposed some approaches to get the compromise solution to the problem and the most reliable shortest path. IV. Compromise solution In the previous discussion, two shortest paths are obtained considering the α-pessimistic values and α-optimistic values of the rough weights. But, sometimes the decision maker may wants to have one shortest path with certain compromise with the optimistic or pessimistic values. In this section we propose two compromise shortest paths. A. L-shortest path To deal with uncertainties of the weights attached to the edges, rough weights are attached to each edge. Let αij = α-optimistic value of the rough weight wij attached to the edge (i , j) βij = α-pessimistic value of the rough weight wij attached to the edge (i , j) Then the interval (αij , βij) can be considered as an uncertain interval in which the value of the actual weight lies with trust level α. We define an uncertain variable ξ in the interval (αij , βij) whose distribution function is defined as a linear regular uncertain distribution as defined bu Liu[11] 0 if x ≤ α ij  α x −  ij φ (x ) =  if α ij ≤ x ≤ β ij β − α ij  ij 1 if x > β ij  The inverse linear uncertain distribution will give a crisp value wij as

φ −1 ( wij , α ) = (1 − α )α ij + αβij In the above process, a set of deterministic weights can be generated from the rough weights of the edges. Using these crisp values, we can obtain a shortest path using Dijkstra’s algorithm which can be considered as a compromise path, where the total weight of the path may be greater than the optimistic value and less than the pessimistic value. This path may be considered as L-shortest path. 4.2 Z-shortest path Another compromise shortest path can be obtained using zigzag uncertain distribution.

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If wij = ([aij , bij] , [cij , dij]) be the rough weight attached to the edge (i , j), then the expected value of wij is defined as γij = E(wij) = (aij + bij + cij +dij)/4 So, the rough weight of the edge corresponds to a triplet (αij , γij , βij) , where αij , βij are α-optimistic and αpessimistic values respectively. Corresponding to this triplet we can define zigzag uncertainty distribution Z(αij , γij , βij) whose distribution function is defined as (Liu[11[) if x ≤ α ij 0   ( x − α ij ) if α ij ≤ x ≤ γ ij   2 ( γ ij − α ij ) φ (x) =   ( x + β ij − 2 γ ij ) if γ ij ≤ x ≤ β ij  2(β − γ ) ij ij   1 if x ≥ β ij

The inverse zigzag uncertain distribution will generate a crisp value wij as if α < 0.5  (1 − 2α )α ij + 2α γ ij ϕ − 1 ( w ij , α ) =  if α ≥ 0.5  (2 − 2α ) γ ij + (2α − 1) β ij In the above process, a set of deterministic weights can be generated from the rough weights of the edges. Using these crisp weights and using Dijkstra’s algorithm, we can obtain a shortest path which can also be considered as a compromise shortest path. This path may be considered as Z-shortest path. V. Reliability analysis Out of the shortest paths proposed, the decision maker may prefer the one which is more reliable or less uncertain. Y. H. Kim et al [13] have studied the reliability of a directed path in an oriented network. Initially, they have determined the directed paths from the connection matrix and computed reliability using the basic theory of probability of events. If Rstj denotes the jth path from source vs to destination vt, then the probability of success Pj, of the path Rstj is given by the product of the reliabilities of the links in series which identify the path Rstj.

Pj =

∏ p, i

where {li ∈ Rstj } is the set of links in path Rstj

li ∈Rstj

If the path do not contain any common links, (that is, the path are parallel), then the reliability of the system is obtained directly from k

R = 1 − ∏ (1 − Pi ) i =1

where, k is the number of links from vs to vt. As per the above definition of Kim et al[13], we define the uncertain reliability of path in a network as below. Considering the rough variable as ([a , b] , [c , d]) with c ≤ a < b ≤ d, we define the measure of uncertainty as Uncertain measure = M =

q− p d −c

where, q and p are the α-pessimistic and α-optimistic values respectively. It is obvious that if the difference between the α-pessimistic and α-optimistic values is less, then the value is more reliable. We define the uncertain reliability of the rough variable as Uncertain reliability = P =

1−

|q− p| |c−d |

so that 0 < P < 1 The uncertain reliability of the path is defined as k

R =1−

∏ (1 − P ) i

i =1

where, k is the number of edges in the path from the source node to destination node and Pi is the uncertain reliability of the ith edge in the path. Further, to find the most reliable path, we find k-shortest paths of minimum number of edges by using any standard algorithm. We consider the minimum number of edges in the k-shortest paths, because if number of edges increases, the weight of the path shall increase substantially though the reliability of the path increases. So

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for better comparison of the paths so far as reliability is concerned, we select k-shortest paths of minimum number of edges. The reliability of the each type of paths can be obtained and the decision maker has got one more choice to select the most reliable path (having maximum reliability) with a compromise of the weights. VI. Numerical example In this section, we have given one numerical example to illustrate our proposed shortest paths. Xiaoyu et al [5 ] have taken one numerical example, where they have taken trapezoidal fuzzy number as the uncertain weight of the edges which has also been used by Yong Deng et al [8] in their paper. We have taken up the same problem for a better comparison. They have taken (a, b, c, d) as trapezoidal fuzzy number where a < b < c <d. We have consider the rough variable as ([b , c], [a ,d]). The graph is given in Fig-1, the rough weights and the related parameters are given in Table 1. The source node is 1 and the destination node is 23.

Figure 3: Network Table 1 Different values (α=0.8) Edge

Weight

α-optimistic value

(1,2) (1,3) (1,4) (1,5) (2,6) (2,7) (3,8) (4,7) (4,11) (5,8) (5,11) (5,12) (6,9) (6,10) (7,10) (7,11) (8,12) (8,13) (9,16) (10,16) (10,17) (11,14) (11,17) (12,14) (12,15) (13,15) (13,19) (14,21) (15,18) (15,19) (16,20) (17,20) (17,21) (18,21)

([13,15],[12,17]) ([11,13],[9,15]) ([10,12],[8,13]) ([8,9],[7,10]) ([10,15],[5,16]) ([11,12],[6,13]) ([11,16],[10,17]) ([20,22],[17,24]) ([10,13],[6,14]) ([9,11],[6,13]) ([10,13],[7,14]) ([13,15],[10,17]) ([8,10],[6,11]) ([11,14],[10,15]) ([10,12],[9,13]) ([7,8],[6,9]) ([8,9],[5,10]) ([5,8],[3,10]) ([7,9],[6,10]) ([13,16],[12,17]) ([19,20],[15,21]) ([9,11],[8,13]) ([9,11],[6,13]) ([14,16],[13,18]) ([14,15],[12,16]) ([12,14],[10,15]) ([18,19],[17,20]) ([12,13],[11,14]) ([9,11],[8,13]) ([7,10],[5,12]) ([12,14],[9,16]) ([10,11],[7,12]) ([7,8],[6,10]) ([17,18][15,19])

13.2857 11.1 10 8.05 9.4 8.8 11.75 19.8 9.2 8.8 9.8 12.8 8 11.375 10.2 7.05 7.0 5.24 7.2 13.375 17.4 9.2857 8.8 16 13.6 12 18.05 12.05 9.285 7.24 11.8 9 7.12 16.6

αpessimistic value 15 12.9 11.7143 8.95 13.937 11.775 15.25 21.822 12.4 10.822 12.46 14.86 9.714 13.625 11.8 7.95 8.833 7.76 8.8 15.625 19.8 11 10.822 16.285 14.88 13.714 18.95 12.95 11 9.76 13.822 10.833 8.4 17.88

Average value

Φ-1(α) for L(a , b)

14.25 12 10.75 8.5 11.5 10.5 13.5 20.75 10.75 9.75 11 13.75 8.75 12.5 11 7.5 8 6.5 8 14.5 18.75 10.25 9.75 15.25 14.25 12.75 18.5 12.5 10.25 8.5 12.75 10 7.75 17.25

14.657 12.54 11.3714 8.77 13.03 11.18 13.5 21.4178 11.76 10.4178 11.928 14.4178 9.37143 13.175 11.48 7.77 8.4667 7.256 8.48 15.175 19.32 10.657 10.417 15.657 16.624 13.371 18.77 12.77 10.657 9.256 13.4178 10.466 8.144 17.624

Φ-1(α) for Z(a,b,c) 14.7 12.54 11.3286 8.77 12.962 11.265 14.55 21.393 11.74 10.393 11.876 14.393 9.3285 13.175 11.48 7.77 8.5 7.256 8.48 15.175 19.38 10.7 10.393 15.7 14.628 13.328 18.77 12.77 10.7 9.256 13.393 10.5 8.14 17.628

Reliability

0.6571 0.7 0.6571 0.7 0.5875 0.575 0.5 0.711 0.6 0.7111 0.62 0.7111 0.6571 0.55 0.6 0.7 0.6333 0.64 0.6 0.55 0.6 0.657 0.7111 0.657 0.68 0.657 0.7 0.7 0.657 0.64 0.7111 0.633 0.68 0.68

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(18,22) (18,23) (19,22) (20,23)

([5,7],[3,9]) ([7,9],[5,11]) ([16,17],[15,19]) ([14,16],[13,17])

5.1 7.1 16.12 14.2

6.9 8.9 17.4 15.8

6 8 16.75 15

6.54 8.54 17.144 15.48

6.54 8.54 17.14 15.48

0.7 0.7 0.68 0.6

(21,23)

([15,17],[12,18])

14.4

16.65

15.5

16.2

16.19

0.625

(22,23)

([5,6],[4,8])

5.12

6.4

5.75

6.144

6.14

0.68

Using Dijkstra’s algorithm, four shortest paths are obtained taking α = 0.8. Using Yen algorithm [15] we have found k-shortest paths having same number of minimum edges. The table 2 shows the different shortest paths, their weights under different parameters and their reliability. So the decision maker has option to choose the one as per his choice. Table 2: Types of Shortest paths Type of path α- optimistic 1st shortest path 2nd shortest path 3rd shortest path α- pessimistic 1st shortest path 2nd shortest path 3rd shortest path Linear 1st shortest path 2nd shortest path 3rd shortest path 4th shortest path Zigzag 1st shortest path 2nd shortest path 3rd shortest path 4th shortest path

Path

Weight

Reliability of the path

1-5-11-17- 21-23 1-5-11-17-20-23 1-5-12-15-18-23

48.17 49.85 50.84

0.9969 0.9951` 0.9971

1-5-11-17- 21-23 1-5-12-15-18-23 1-5-11-17-20-23

57.282 58.59 58.865

0.9960 0.9971 0.9951

1-5-11-17- 21-23 1-5-11-17-20-23 1-5-12-15-18-23 1-5-11-14-21-23

55.459 57.061 59.008 60.325

0.9969 0.9951 0.9971 0.9956

1-5-11-17- 21-23 1-5-11-17-20-23 1-5-12-15-18-23 1-5-11-14-21-23

55.369 57.019 57.031 60.306

0.9969 0.9951 0.9971 0.9956

From this numerical example, it is seen that in all four types one path 1-5-11-17-21-23 comes as the shortest path. But so far as reliability is concerned, another path 1-5-12-15-18-23 becomes the most reliable shortest path in all four types VII. Conclusion In this paper, we have proposed four types of shortest paths from the source node to destination node where the edges are attached with rough weights. Initially, α- optimistic and α- pessimistic shortest paths are obtained as two possible shortest paths. Further, two models are proposed for one compromise shortest path using linear and zigzag uncertain distribution. Then uncertain reliability of the path is defined and reliability of the paths is obtained finding k-shortest paths of minimum number of edges in all types. It is found that one path in all four cases comes to be the shortest path. But another path in all four cases comes to be the most reliable shortest path, though the weight is slightly high. So the option lies with the decision maker to choose either of the paths. Further, the result obtained in this work is found to be better than the result obtained by Xiaoyu et al [5 ] by suitably converting the fuzzy trapezoidal numbers in to rough number. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

Dubois, D, Prade, H. Fuzzy Sets and Systems. Theory and Application, Academic Press, New York (1980) Klein, CM, Fuzzy shortest paths. Fuzzy set system, 39, 27-41 (1991) Yagar, R, Paths of least resistance on possibilistic production systems. Fuzzy Set. Syst, 109, 121-132 (1986) Okada, S, Soper, T. A shortest path problem on a network with fuzzy arc lengths. Fuzzy Set System 109(1), 129-140 (2000), Xiaoyu Ji, Kakuzo, Iwamura, Zhen Shao, New models for shortest path problem with fuzzy arc lengths. Applied Mathematical Modelling, 31, 259-269 (2007) Frank, H. Shortest paths in probability graphs, Operation Res 17, 583-599 (1969) Fu, L, Rilett, LR Expected shortest paths in dynamic and stochastic traffic networks. Transport Res 32(7), 499-516 (1998) Yong Deng, Yuxin Chen, Yajuan Zhang, Sankaran Mahadevan, Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment. Applied Soft Computing 12, 1231-1237 (2012) Pawlak, Z. Rough sets. International Journal of Information and computer Scince, 11(5), 341-356 (1982) Pawlak, Z. Rough Sets, some extension. Information Sciences, 177, 28-40 (2007) Liu, B: Uncertainty Theory: Springer-Verlag, Berlin (2007) Liu, B: Uncertainty Theory: An Introduction to its axiomatic Foundation. Sringer-Verlag, Berlin (2004) Kim, YH, Case, KE, Ghare, PM. A method for computing complex system Reliability. IEEE Tran. Reliability 21, 215-219 (1972) Mirchandani,PB. Shortest distance and reliability of probabilistic networks. Comut. & Ops Res,3, 347-355 (1976) Yen, JY, Finding the k-shortest paths in a network. Management Science 17, 712-716 (1971)

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A NOTE ON VOLUME OF PARALLELOPIPED Sameen Ahmed1 and Sandeep Suman2 Asst. Prof., S.N.A. College, Barh (Patna), Bihar, India 2 Guest Lecturer, Govt. Pharmacy College, Patna, Bihar, India 1

Abstract: Determinant gives an n-dimensional volume spanned by n-independent vectors in  n . In this article we work out a method of finding the k-dimensional volume spanned by k-independent vectors in

 n such that k  n. Keywords: Determinant, Parallelopiped , k-dimensional Volume I. INTRODUCTION Volume is scalar quantity associated with geometric objects. A parallelopiped is one of the simplest object in

 n . More complex objects may be approximated by these parallelepiped. Def (Parallelepiped): A parallelopiped u1 , ..., u n  spanned by k-independent vectors v1 , ..., v k is the set of all points in  n satisfying following condition:







i 1



v1 ,..., v k : v  n | v   i vi | 0  i  1 Now, we can define the signed volume of parallelepiped with following properties, i.e.,

vol:n  ... n  . I. If we multiply one side of parallelepiped with non-zero scalar  then it’s volume will be multiplied by same amount.

vol[v1 ,...,vi ,...,v n ]  vol[v1 ,...,vi ,...,v n ]

 vi

vi 2.

vol[v1 ,...,vi  v j ,...,v n ]  vol[v1 ,...,v n ] Cut and Paste

vj v +i

vol[e1 ,...,e n ]  1

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All these properties derived from the geometric idea. To compare it with determinant we need extra property of skew-symmetry. 4. vol[v1 ,...,vi ,...,v j ,...v n )  vol[v1 ,...,v j ,...,vi ,...,v n ] Now all these property 1-4 satisfies the definition of determinant given by Curtis (1984). Hence, vol([v1 ,...,v n ] )  det (v1 ,...,v n ) Theorem: Let W be some k-dimensional subspace of  n , then there exist some orthogonal matrix such that, it transforms W to a subspace of  n of the form k  0. Proof: Choose any orthonormal basis of W such that b1 ,...b k . Extend this basis to form a basis of

 n , (b1 ,..., b n ) . Now, Define a linear transformation T : n  n , such that T(bi )  ei . This transformation is basically change of basis. To compute the matrix associated with respect to T. Let’s compute T

–1|

–

T 1 : n  n –

II. MAIN RESULT Volume of k-dimensional parallelepiped formed by k-independent vectors. x1 ,...,x k will be given by the following formula V [ x1 ,...,x k ]  det ( x T x )

where x is n  k dimensional matrix formed by columns of

xi . Proof: Given vectors x1 ,...,x k ,  A  O (n) . Such that

  Ax i   y i  such that y i k O |  |  det Y Now, vol [ x1 ,...,x k ]   det  y1 ... y k  |  k k | Now, (Ax) T (Ax)  Y T Y .





Taking, x T A T Ax  Ax  Ax  T

 x T x  YT Y  det (x T x)  det (Y)T det (Y)

 det (x T x)  det (Y)  det (Y)  det (Y)

 det (Y)  det (x T x) Hence,

vol[x1 ,...,x k ]  det (x T x)

III. CONCLUSION The volume of k-dimensional parallelepiped in terms of determinant is obtained. Determinants are relatively simple to calculate using Gaussian Elimination. Thus the volume of parallelepiped can be determined easily. REFERENCES [1]. [2]. [3].

Curtis, Charles W., “Linear Algebra: An Introductory Approach”, Springer-Verlag, New York, Inc., 1984. Kumaresan, S., “Linear Algebra: A Geometric Approach”, Prentice-Hall of India Limited, New Delhi, 2000. Saikia, P.K., “Linear Algebra”, Dorling Kindersley (India) Pvt. Ltd., 2009.

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Characterization of alumina catalyst in the catalytic fuel reformer M. Kannan* and Dr. C.G. Saravanan Department of Mechanical Engineering, Annamalai University Annamalai Nagar â&#x20AC;&#x201C; 608 002, Tamil Nadu, India. Abstract: The catalytic fuel reformer (CFR) is investigated in this study. Waste engine oil (WEO) is used as the fuel for CFR and alumina is used as the catalyst to crack the WEO. The reformulated gas is condensed using water cooled condenser and analyzed to know the suitability of using this reformulated gas in the compression ignition engine. The condensed reformulated gas is named as WEOA. The different properties such as specific gravity, kinematic viscosity, flash and fire point, gross calorific value, pour point, density of WEOA is analyzed and compared to that of diesel fuel and found that all the properties are closer to that of diesel fuel. WEOA is characterized by GC-MS and the result revealed that the heavier hydrocarbon presents in the WEO are cracked into light hydrocarbon because of the catalysts alumina and which is similar to that of diesel fuel The FT-IR analysis was also conducted and the result revealed that the major transmittance spectrums peak are acorns and the presence of the hydrocarbon is clearly seen in the WEOA. Based on the investigation, it is suggested that WEOA has a potential to be used as alternate fuel for diesel engine. Hence an environmentally unfriendly WEO can be recycled into a useful resource and serves as an alternative source of fuel for diesel engine. Keywords: Waste engine oil, fuel reformer, pyrolysis, alumina, catalyst

I. INTRODUCTION Exhaustible fossil fuel reserves and steep climb in fuel price has resulted in a continuous hunt for potential alternative fuels for internal combustion engines. The search is very particular to find suitable alternative fuels for compression ignition engines (CI) as they are widely used in many applications. The production of waste engine oil was estimated as 24 millions of tons per year around the world. The disposing of waste engine oil is a serious event for the environment because of its hazards in nature. Early method of discarding the waste engine oil such as incineration, combustion for energy recovery, vacuum distillation and hydro treatment is likewise experiencing the problem of throwing away the sludge as the terminal product of these processes [1-3]. Waste engine oil contains a variety of aliphatic and aromatic hydrocarbons [4] and hence already been investigated as the alternate fuel for the compression ignition engine [5] which increases the brake thermal efficiency and cuts back the specific fuel consumption. Pyrolysis techniques as an economic and environmental friendly method of recycling the waste engine oil [6-8] into useful alternate fuel for compression ignition engine. The waste material was thermally cracked and decomposed in an inert atmosphere, with resulting pyrolysis oils and gases able to be used as a fuel. The oil can be catalytically upgraded or refined in the petroleum processing industries for further process [9]. In this operation, the waste engine oil was thermally cracked in the catalytic fuel reformer (CFR). As a consequence of heating the oil is thermally cracked in the absence of oxygen into shorter hydrocarbon chains. The composition of and physiochemical properties of pyrolysis oils vary and mostly depend on the feedstock used and processing technology employed. The features of these oils directly relate to their behavior in fuel systems and locomotive operation. Thus, it is indispensable to carry out characterization and evaluation of pyrolysis oils before their employment as an engine fuel. The fuel injection characteristics such as injection timing, injection pressure and injection duration largely depend on the oil density and viscosity because of their influence on oil atomization effects during injection [10,11]. Early research proves that a relatively low density of the oil retarded injection timing, while a relatively low viscosity results in an advanced timing because of less friction produced by the oil travelling through the nozzle [11, 12]. Aluminum oxide, Al2O3 is a major engineering material. It offers a combination of good mechanical properties and electrical properties leading to a wide range of applications. Its high hardness, excellent dielectric properties, refractoriness and good thermal properties make it the material of choice for a wide range of

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applications. In its largest scale application, aluminium oxide is the catalyst in the Claus process for converting hydrogen sulfide waste gases into elemental sulfur in refineries. It is also useful for dehydration of alcohols to alkenes. Aluminium oxide serves as a catalyst support for many industrial catalysts, such as those used in hydrodesulfurization and some Ziegler-Natta polymerizations. The purpose of the present investigation is to detect the suitable alternate fuel for compression ignition engine and also disposing the waste engine oil (WEO) the hazardous waste in nature by reuse it as substitute fuel. Hence this study investigates the characterization of the WEO after it was heated in the CFR, to know the suitability as alternate fuel in compression ignition engine. This work also traces the influence of the catalyst alumina used on the CFR. Some of the significant attributes of the oil are broken down and compared to those of conventional diesel fuel. II. MATERIALS AND METHODS A. REFORMULATED FUEL PRODUCTION SYSTEM A CFR was designed and fabricated to convert the WEO into diesel like fuel. The reformer is installed in the Engine Research Laboratory, Department of Mechanical Engineering, Annamalai University. Schematic representation of the system is shown in figure 1.

Figure 1. Catalytic Fuel Reformer The system consisted of several components such as fuel tank, control panel, reactor, thermocouple, stirrer, condenser, fuel storage tank. The fuel tank is used to supply the WEO into the reactor. The reactor of the system has a cylindrical shape with inner diameter of 15cm and the length of 45cm. The reactor was designed and fabricated to heat the WEO along with the catalyst. It includes an electrical heating unit which can be employed to heat the WEO with catalyst upto 1000oC. The electric heater has resistance heater and a voltage control which is used to adjust the heating rate. The heating control is done by the control panel. The stirrer is used to mixing the WEO with catalyst uniformly and also to distribute the temperature uniformly. The thermocouple is used to measure the temperature in the reactor. The condenser unit is used to condense the reformulated gas from the reformer. Alumina pellets of 2 to 5 mm diameter is used as catalyst in the CFR. The CFR is set to 300oC. The condensed reformulated gas is named as WEOA. B. COMPOSITIONAL ANALYSIS B.1. GC-MS Liquid samples were dissolved in methanol and analyzed using GC-MS instrument (Varian-Saturn-2200 MS/MS). The GC-MS was operated in non-isothermal mode, ramping at 250 oC using a 30 m fused silica capillary column (cross linked 5% PH ME siloxane, I.D. 0.25 mm film thickness 0.25 Âľm).

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The total ion chromatogram produced for each sample was analyzed using Varian analysis software and the NIST mass library. The chromatograph integrator was programmed in two different modes, allowing the quantification of compounds by both species and size. In this manner, a single GC-MS analysis permitted the identification of the products and the categorization of the sample by chain length. The GC-MS was not calibrated for the individual compounds in the samples; hence, the compounds are quantified as total ion content percentage (TIC%) - an integration of the chromatogram peaks. B.2. FT-IR A Bruker-Alpha FT-IR spectrometer with a resolution of ±1 cm−1 was used. Spectra were recorded at room temperature (298 K) in the region of 4000 to 400 cm−1 and NaCl cell of path length 0.1mm was used. The spectrometer possesses auto align energy optimization and dynamically aligned interferometer. It is fitted with a KBr beam splitter, a DTGS-Detector and Everlgo™ mid-IR source. A baseline correction was made for the spectra recorded. III. RESULT AND DISCUSSION A. FUEL CHARACTERIZATION Fuel properties like density, kinematic viscosity, flash point, fire point, calorific value and specific gravity are analyzed for FEO, WEO, WEOA and compared it to that of diesel fuel. The density is evaluated according to ASTM D1298 method, kinematic viscosity is measured according to ASTM D445 method, flash level and fire point are measured as per ASTM D93, calorific value is assessed as per ASTM D5865 method, specific gravity is measured as per ASTM D1298 method and calorific value is assessed as per ASTM D240. Some of the important properties of diesel fuel, FEO, WEO and WEOA are shown in table 1. Table 1 Properties of diesel, WEO and WEOA Property Specific gravity @ 27C Kinematic viscosity @40C in CSt Flash Point inC Fire Point in C Gross calorific value in MJ/kg Density@15C in gm/cc

Diesel

WEO

WEOA

0.8298 2.57 50 56 44.67 0.8072

0.879 52 197 45.4 0.858

0.9098 11.10 30 33 45.13 0.9090

B. CHEMICAL COMPOSITION B.1 GC-MS WEOA is characterized by GC-MS. The results of GC-MS analysis was shown in figures 2. The GC-MS result revealed that the WEO containing C11 - C40 hydrocarbons, was thermally cracked with alumina catalyst to WEOA comprising mainly of C5 – C30 hydrocarbons and which are dominated by aliphatic hydrocarbons and significant amounts of aromatic. This indicates the occurrence of cracking of compounds to produce some aromatic structures. Perhaps derived from cyclisation and aromatization reactions that occurred during pyrolysis. The aliphatic hydrocarbons were mostly11:25 alkanes. MS Data Review All Plots - 8/26/2013 AMThese aliphatic hydrocarbons, particularly the C 5 – C20 aliphatic fractions, represent a potentially high value fuel source. File: d:\2013-staff\mgk\s3.sms Sample: S3 Scan Range: 1 - 2090 Time Range: 0.00 - 33.98 min.

Operator: raja Date: 8/21/2013 4:56 PM

kCounts

S3.SMS 50:500 50:500

6.686 min

125

6.067 min

100

18.039 min

14.320 min

17.052 min

16.016 min

6.131 min

5.925 min

14.939 min

6.227 min

+ 5.010 min

0 5.0

7.5

Spectrum 1A BP: 94.9 (104=100%), s3.sms 94.9 104 100%

10.0

12.5

15.0

17.5

minutes

6.324 min, Scan: 384, 50:500, Ion: 1502 us, RIC: 395, BC Figure 2 GC-MS of WEOA

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B.2 FT-IR FT-IR spectroscopy was used to identify the functional groups present in waste engine oil and it can offer information regarding the chemical change of the functional groups which may play an important role in investigating the influence of catalyst with waste engine oil. The representative FT-IR spectra of the WEOA is shown in figure 3. Typical infrared absorption bands of hydrocarbons can be observed. Example, between 3000 and 2800 cm -1 the presence of C-H stretching vibration of -CH2- and -CH3 groups was blamed for infrared activity. On the other hand significant differences between the ratio of -CH2- and -CH3 groups can be observed, because the intensity of asymmetric stretching, vibration bands at 2926 cm-1 and 2962 cm-1 is the proportional number of -CH2- and CH3 groups, respectively. 110

100

Transmittance (%)

50 4000

3500

3000

2500

2000

1500

1000

500

-1

Wave Number (cm )

Figure 3 FT-IR of WEOA The infrared activity in the wave number range of 1470 – 1430 cm-1 and 1395 – 1365 cm-1 was caused by the asymmetric and symmetric deformation stretching of -CH3 groups. One band was found in the range of 1650 – 1750 cm-1, which could be occurred from the vibration of benzene derived aromatic compounds. It is known that the position of that peak varies with the structure of subsistence. In the wave number range of 800 – 1000 cm-1, the C-H stretching vibrations caused infrared bands at 910 and 990 cm-1 from the vinyl type double bonds, at 890 cm-1 from the vinylidene and at 956 cm-1 from the internal positioned double bonds. Infrared band in the range of 800 – 500 cm-1 is induced by the mowing vibration of -CH2- groups and the aromatic. Hence FT-IR results confirmed that most of the hydrocarbons found in the WEOA are alkanes and thus a potential to be used as alternate fuel in diesel engine. IV. CONCLUSION Alternate fuel for diesel engine is the important factor of the environment due to the diminishing of fossil fuels, increasing price and awareness of the increased environmental consequences of emissions from diesel engines. In the present study, the possibility of using WEO as diesel like fuel was investigated. The collected WEO was allowed into the CFR. Two different catalysts red mud and fly ash were used in this investigation. The reformed gas from the CFR was condensed using condenser and the sample was analyzed. The following results were obtained: 1. Characteristics of the reformulated fuel, such as density, flash point, fire point, viscosity, calorific value is tested and found to be close to that of the diesel fuel. 2. Alumina is used as catalysts. Based on their individual properties of the catalyst, it efficiently utilized to convert the WEO into diesel like fuel. 3. The GC-MS results revealed that the heavier hydrocarbon presents in the WEO were cracked into light hydrocarbon because of the catalysts alumina and which is similar to that of diesel fuel.

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FT-IR results confirmed that most of the hydrocarbons found in the WEOA are alkanes and thus a potential to be used as alternate fuel in diesel engine.

[1]

Kim, SS,; Chun, BH.; and Kim, SH. Non-isothermal pyrolysis of waste automobile lubricating oil in a stirred batch reactor. Chemical Engineering Journal 2003; 93(3), 225-231. M.J. Fuentes, R. Font, M.F. Gomez Rico, I Martin Gullon, Pyrolysis and combustion of waste lubricant oil from diesel cars: Decomposition and pollutants, J. Anal. Appl. Pyrolysis 79 (2007) 215–226. Ludlow-Palafox C, Chase HA. Microwave-induced pyrolysis of plastic wastes. Industrial and Engineering Chemistry Research 2001;(40(22)), 4749-56. Brinkman, DW.; and Dickson, JR. Contaminants in used lubricating oils and their fate during distillation/hydrotreatment rerefining. Environ Sci Technology 1995; 29, 81-86. Tajima H, Combustion of used lubricating oil in a diesel engine, SAE : 2000; Paper no. 2001-01-1930. Song G.J., Seo Y.C., Pudasainee D, Kim I.T., Characteristics of gas and residues produced from electric arc pyrolysis of waste lubricating oil. Waste Management 2010; 30, 1230 – 1237. Lam SS., Russell AD., Chase HA., Pyrolysis using microwave heating : a sustainable process for recycling used car engine oil. Ind. Engg. Chem. Research 2010; 49, 10845 – 10851. Sinag A, Gulbay S, Uskan B, Ucar S, Ozgurler SB, Production and characterization of pyrolytic oils by pyrolysis of waste machinery oil. J. Hazardous Material 2010; 173, 420 -426. Lam SS., Russell AD., Chase HA., Microwave pyrolysis, a novel process for recycling waste automotive engine oil. Energy 2010; 35, 2985 – 2991. Yamane K, Ueta A, Shimamoto Y. Influence of physical and chemical properties of biodiesel fuels on injection compression ignition engine. In : The 5th international symposium on diagnostics and modelling of combustion in internal combustion engines : COMODIA 2001; Jul 1-4, Nagoya, 2001. Torres-Jimenez E, Dorado MP, Kegl B. Experimental investigation on injection characteristics of bioethanol-diesel fuel and bioethanol-biodiesel blends. Fuel 2011; 90 (5), 1968 – 1979. Torres-Jimenez E, Svoljsak-Jerman M, Gregorc A, Lisec I, Dorado MP, Kegl B. Physical and chemical properties of ethanoldiesel fuel blends. Energy Fuel 2010; 24 (3), 2002 – 2009.

References [2] [3] [4] [5] [6] [7] [8] [9] [10]

[11] [12]

About Authors: M. Kannan, Assistant Professor in the Department of Mechanical Engineering, Annamalai University, Annamalainagar, Tamilnadu, India. He received M.E degree from Annamalai University in 2007. His area of specialization is IC engines, Alternative fuels, Emission control techniques, Automobiles. He had experience in research at alternative fuel for IC engines. He published two international journals and one national journal.

Dr.C.G. Saravanan, Professor of Mechanical Engineering, Annamalai University, Annamalainagar, Tamilnadu, India. He received his Ph. D from Anna University, Chennai. He guided about 10 canditates. He published more than 50 international journals and 60 national journals. His area of specialization is IC engines, Emission control techniques and alternate fuels.

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A direct mathematical method to calculate the efficiencies of 4πNaI (Tl) scintillation detector Salam Noureldine1 and Yousef H. Ajeeb2 1, 2 Basic Science Department, Faculty of Science, Arts, Science and Technology University of Lebanon, Beirut, Lebanon. Abstract: A direct mathematical method is applied to calculate the total efficiency of a 4π NaI(Tl) scintillation detector for an arbitrarily positioned radiating point source. The 4π NaI(Tl) scintillation detector is extremely important to measure the activities of low radioactivity samples. The central square is left void, so the radiating source can be moved easily inside the detector in which the total efficiency is measured at different positions and energies. The attenuation of the photon by the detector active volume and the detector end-cap materials are also included in the integrals. The calculated values of the efficiency are found to be in good agreement with the experimental one. Keywords: 4πNaI (Tl) scintillation detector, Total efficiency, Direct mathematical method, Parallelpiped detector. I. Introduction Scintillation detectors are widely used for gamma-ray detection. Gamma rays could be emitted from point, line, disc or volumetric sources. The detection of these radiations is done by different detectors [1-9]. These detectors were designed with different efficiencies, depending on the shape of the detector and on the material of its active medium. The total efficiency for the source – detector system is determined experimentally and theoretically [6]. In our present work we will take into consideration the photon path length through the detector active medium in order to determine the efficiency of the 4πNaI (Tl) scintillation detector [10]. The work described below involves the use of straight forward analytical formulae [10] for the computation of the 4πNaI(Tl) detector and total efficiency. Section 2 presents direct mathematical formulae for the total efficiency in the case of isotropic radiating axial point, non-axial point, plane and volumetric sources. Section 3 contains comparisons between the calculated efficiency using the formulae derived in this work with the published experimental and simulated values illustrating the validity of the present mathematical formulae. The conclusion is presented in section 4. II. Mathematical viewpoint The position of the isotropic point source is defined by (ρ,h), where,  is the lateral displacement between the source and the detector axis, whereas h is the height of the source from the detector center. The polar (θ) and azimuthal (φ) angles represent the direction of the photon when it enters the detector active volume. The effective rays which enter the detector active volume traverse a distance d until it emerges from detector [10]. A. Axial point source placed inside the detector void part The incident photon may enter the detector’s inner side and emerges from see fig.1

Fig. 1: Schematic view of 4πNaI (Tl) parallelepiped scintillator detector with central part left void and all the possible path lengths through the active medium of a parallelepiped (a×b×c) detector for a photon emitted from an isotropic axial radiating point source.

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i) d1a ,b

lower base 1(LB1) and 2 (LB2): a, b c   2 sin(  ) cos( ) cos( )

(1)

ii) detector side (1) and (2): d 2a , b 

a, b  a1 , b1 2 sin(  ) cos( )

(2)

The polar angle θ takes the steps  a ,a  1a1 , a  tan 1  1   2c 

(3)

 b1 , b    2c 

 2b1 , b  tan 1 

(4)

The azimuthal angle φ takes the steps n  n

n  n



b  tan 1   2 a

 2

a  tan 1   b

n=0,2

(5)

n=1,3

(6)

The total efficiency can be calculated by a

 0 1

1    0 

a1 1

 2 2

   0 

a,b

a 1

f1a d

 0 2

d    0

a1 1

f 2a d

f1a d

 (1 - e

1  2

d   

-dia ,b

0 

b1 2

1  2b

d    0 

b1 2

f1b d

 1 2

d    0 

f1b d

) sin(  ) . e

3  2

d    2 

1.t sin( )

b1 2

f 2b d

d    f 2b d d

2  1

d    3 

a1 1

1 

(7)

a1 1



f1b d

 2 2

f1a d

2 2

d    f 2a d d 0  1a

, i=1,2….4.

(8)

where, μ and μ1 are the total attenuation coefficient of the detector and its housing, with thickness t, material for gamma-ray photon with energy Eγ, respectively. B. Non-axial point source placed inside the detector void part The incidence photon may enter the detector’s inner side and emerges from see fig. 2

Fig. 2: Schematic view of all the possible path lengths through the active medium of a detector for a photon emitted from an isotropic non-axial radiating point source.

parallelepiped ( a×b×c)

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Salam Noureldine et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), JuneAugust, 2014, pp. 132-138

lower base 1 (LB1):

d1  ii)

c a  2  cos( )  1 cos( ) 2 sin( ) cos( )

(9)

detector side (1), (2),(3) and (4):

d 2a,b  iii)

a, b - a1 , b1 2 sin(  ) cos( )

(10)

lower base 2 (LB2):

d3  iv)

 c a1  2  cos( ) cos( ) 2 sin(  ) cos( )

(11)

lower base 3 (LB3) and 4 (LB4)

d 4 

c b1  sin( )  cos( ) 2 sin( ) sin( ) sin( )

(12)

The final expression of the total efficiency of a non-axial point source at different positions is given by: 11

2 1

 2 2

 2 2

0  a1

4  a1

0 a

4  a

 2    f1 d d    f1 d d    f 2 d d    f 2 d d 1

4 3

 4 2

3  b1

3  b

3  4

 3 2

 2 2

2  2

2  a1

2  a

1  2b

1  b1

   f3 d d    f 4 d d    f5 d d    f6 d d

(13)

   f7 d d    f8 d d 3

The polar angle θ takes the steps a , a - 2  cos( ) 1a1 ,a  tan - 1 1 2.c. cos( ) b , b - 2  sin(  )  2b1 ,b  tan - 1 1 2.c. cos( )

b , b  2 sin(  )  3b1,b  tan - 1 1 2.c. sin(  ) a , a  2  cos( )  4a1 ,a  tan - 1 1 2.c. cos( )

The azimuthal angle φ takes the steps  a - sin(  )    1 2   tan   1 b  - cos( )  2   b   cos( )        tan - 1  2  2 b 2  - sin(  )   2  a    sin ( )    1 2     tan   3 b  -  cos( )  2 

(14) (15) (16) (17)

(18)

(19)

(20)

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Salam Noureldine et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), JuneAugust, 2014, pp. 132-138



 a -  cos( )    3 1 2   tan   b 2    sin ( )  2 

(21)

C. Line source placed inside the detector void part The efficiency of a 4πNaI (Tl) scintillation detector arising from a line of length ℓ cm is derived as [2] h2

 1dh

L 

(22)

h2 - h1 where, ξ1 is the total efficiency of an axial point source as identified before in equation (13). The total efficiency of the line source is determined at different positions along the axial axis of the detector. D. Disc source placed inside the detector void part The efficiency of 4πNaI (Tl) scintillation detector arising from a disc source is derived as [2] s

D 

  2 d

(23)

Where, ξ2 is the total efficiency of a non-axial point source as identified before in equation 23. The total efficiency of the disc source is determined at different positions along the axis of the detector. III. Results and Discussion The direct mathematical method is applied to determine the total efficiency of the 4πNaI (Tl) detector using an isotropic point source. The 4πNaI scintillation detector is shown in fig. 1 in which the central part is left void so the radiating source can be moved easily. The cross sectional size and the length of a NaI(Tl) crystal are 0.1 × 0.1 m2 and 0.4 m, respectively. The housing of the 4πNaI (Tl) detector is Stainless steel of 1×10-3 m thick. The solid angle of the detection geometry is high that more than 95% can reach the detector surface. The total efficiency values of a point source placed at different positions along the axial axis and at different energies have been calculated for 4πNaI (Tl) scintillator detector by the direct mathematical method and compared with Byun [6] as shown in figs. 3-4. The total efficiency is also determined at lateral distances ρ from the central axis of the detector as shown in fig. 5. The figure shows no variation of the total efficiency as the lateral distance changes for the same height. Three different sources, a non-axial point, a line source of length 5×10-2 m and disc source of radius 2×10-2 m have been used in the theoretical calculations for this detector. Since the experimental values were in good consistent with direct mathematical method, we extended the study to line and disc source at different heights. The efficiency was studied at an energy E=0.5 Mev, starting from the center where the height was considered zero until it reached the surface at a height of 0.2 m. Figs. 6-8 show that as the height of the non-axial, line and disc sources from the center increases the total efficiency decreases for different sources, which consists with the results obtained by Byun [5]. It is clear also that the total efficiency for disc source is greater than that of line and point sources. The percentage differences between the calculated values and the measured ones, which are given by equation (25), are shown in fig. 3.

Diff (%) 

 cal   meas  *100

(25)

 meas

The squares in figs. 3 show the theoretical total efficiencies while the red circles show experimental total efficiencies. IV. Conclusions: Direct mathematical expressions to calculate the total efficiency of 4πNaI(Tl) scintillator detector have been derived in the case of axial point, non-axial point and extended disk and cylindrical sources. In addition, the attenuation of photons by the source container and the detector housing materials is also presented in simple straight forward mathematical expressions. The agreement between the results calculated in this work and the published values are very good, the high discrepancies being less than 4 % (point source).

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Salam Noureldine et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), JuneAugust, 2014, pp. 132-138

Fig. 3: Variation of relative total efficiency of a point source along the axial axis of the parallelepiped detector at energy 0.5 Mev. Present work Experimental

0.9 0.8

Total Efficiency

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

Energy in MeV

Fig. 4: Variation of total efficiency with photon’s energy and its comparison with the experimental values.

0.5

Total effeciency

0.4

0.3

0.2

0.1

0.0 0

Lateral distance p (cm)

Fig. 5: Variation of total efficiency of a point source placed at the surface the detector versus the lateral distance (ρ).

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Salam Noureldine et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), JuneAugust, 2014, pp. 132-138

0.80 0.75 0.70

Total efficiency

0.65 0.60 0.55 0.50 0.45 0.40 -20

-10

Position(cm)

Fig. 6: Variation of total efficiency of a non-axial point source placed at different distances from the center of the detector. 0.85

Total efficiency

0.80

0.75

0.70

0.65

0.60

-20

-10

Position (cm)

Fig. 7: Variation of total efficiency of a line source placed at different distances from the center of the detector. 1.0

0.9

Efficiency

0.8

0.7

0.6

0.5

-20

-10

Position (cm)

Fig. 8: Variation of total efficiency of a disc source placed at different distances from the center of the detector.

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Salam Noureldine et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), JuneAugust, 2014, pp. 132-138

References: [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Abbas M I, Applied. Radiation and Isotopes, 2001a, 54: 761– 768. Abbas M I, Radiat. Phys. Chem, 2001b, 60: 3–9. Abbas M I and Basiouni M M, American Inst. Phys, 1999, 450: 268–272 Blaauw M, Nucl. Inst. Meth. A, 1998, 419: 146–153. Byun S H, Prestwich W V, Chin K, McNeill F E, and Chettle D R, Nucl. Instr. Meth. Phys. Res. A, 2004, 535: 674-680. Byun S H, Prestwich W V, Chin K, McNeill F E, and Chettle D. R, IEEE Trans. Nucl. Sci. NS-53, 2006, 5: 2944. Nafee S S and Abbas M I, Applied Radiation and Isotopes, 2008, 66: 1474– 1477. Selim Y S, Abbas M I and Fawzy M A, Radiat. Phys. Chem, 1998, 53: 589–592. Selim Y S and Abbas M I, Radiat. Phys. Chem, 2000, 58: 15–19. Wang T K, Hou I M and Tseng C L, Nucl. Instr. and Meth. A, 1999, 425: 504– 515.

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American International Journal of Research in Science, Technology, Engineering & Mathematics

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An Effective Approach to Equivalence the Blood Circulatory System through Human Heart with the Artificial Neural Network Nirmalya Chandra Assistant Professor, Birbhum Institute of Engineering and Technology, Suri, Birbhum, West Bengal, Pin 731101 Abstract: Multi-Layered Network shows the feed forward connection characteristics in mathematical models system. Here, we focus on analysis of neocortical processing of Blood Flow from Heart to Body and vice versa. The Blood Flow in Human body always follows Non Linear Distribution Function. Here in this paper we basically try to solve the Heart Inactive problem by pushing any external trigger or to minimization the possibility of Heart Blocked or Stroked by applying neural based algorithm in this Non Linear network system Keywords: Artificial Neural Network, Blood Circulatory System, Heart Pump Circuit, Gradient Descent algorithm, Pseudo – Inverse Approach I. INTRODUCTION Neural Network based on artificial statistics plays an important role in the process control schemes. The system identification of this network is developed on the basis of mathematical models that are why it can express the network in terms of multinetworks. Now, the circulatory blood flow system through a human body from one organ to another can be realized as a neural network. The bold circulatory system in human body also known as lymph trans port system [ 1 ]. The brain of a human contains a network which inter-dependent among the blood flow system. The inter-dependent network is well co related with neural network. Now trace on blood flow system. The oxygenated blood flow arteries to the organs and the portion of the blood that flows into the kidneys is cleaned of impurities and waste products are activated through the bladder and urethra. De oxygenated blood [ 1 ] moves the right side of the heart at the right atrium. It is then pumped into right ventricle and out heart to the lungs the blood gives up its CO2 and takes a fresh supply of O2. In this paper it have case studied on equivalence of the blood flow circulatory system from hearts to human body and vice versa with the artificial neural network models. This also have shown that how with the basis of apply some algorithms based on neural networks to the blood circulatory system to continuing the activation of Heart Working Function continuously in any unwanted accidental circumstances in Heart if happening. II. THE EVENTS OF ATRIAL AND VENTRICULAR IN CARDIAC CYCLE In Cardiac Cycle there have been seen Atrial and Ventricular events. That is discussed here. (1) Atrial Systole : Its timing states is around 0.1 sec. First half is Dynamic phase ( The Systolation is very much high ) and others half is Adynamic phase ( The Systolation is happening slowly ). For this systole blood is entered by pushing the Tricaspid and Bycaspid valves respectively. (2) Atrial Diastole : Its timing cycle is around 0.8 sec. Blood here flows through 4 No of pulmonary vens and Superior and Inferior vena cavas to the right and left atrial. After this the Atrial Systole starts again. (3) Ventricular Systole : It starts just after the Systole of Atrial. After the 1st Heart sound ( 0.05 sec time required after the close of Atrial – Ventricular valves ) the semileunor valves open. This middle period of this closing and opening valves are known as Isometric Contraction Period. In this period Ventricular are compressed, so the inner membrane of ventricular feels pressure increases. In this time, the blood exits from ventricular through the pulmonary ven and Superior and Inferior vena cavas. This period is known as ejection period. The blood flow rate at the outset very fast then the flow rate decreases. The fast period is maximum ejection period and slow period is known as reduced ejection period. Here in this phase, the Systole of Ventricular is stopped and Diastole of Ventricular starts. (4) Ventricular Diastole : At the starting of this Diastole, the semileunor valf are closed and 2nd Heart Sound is heard. The period between closed of semileunor valf and starting of Ventricular Diastole is known as Protodiastole Period. After the 3rd Heart Sound, the the valves of Atrial and Ventricular open and the blood

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Nirmalya Chandra, American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), June-August, 2014, pp. 139-143

flows from Atrial to Ventricular. The gap between the closed of semileunar valf and opened of AtrialVentricular valf is known as Isometric Relaxation Period. The blood flow around the ventricular consists 3 phases:(a) First Ratrid Filling Phase - Blood flow rapidly down. (b) Diastans – Blood Flow rate slow down. (c) Last Rapid Filling Phase – The rate of blood flow increases again rapidly. After the 4th Heart Sound the diastole of Ventricular phase is end. Then again Systole of Ventricular starts. The Heart serves as a pump [ 1 ]. In the presence of electrical stimules, the heart will contract in the atria which belongs to form a shallow. A fraction of second later, the ventricles also contracts in the form of systole and diastole as discussed earlier. The Heart pumps approximately 3 to 5 litre of blood per minute. The figure of Cardiac Output |( CO ) will be calculated as follows : CO = Heart Rate ( Beats/ min ) * Stroke Volume ( L / Beat ) ….. (1) Stroke Volume is the volume of blood ejected from ventricles during systole. III. TOTAL BLOCK DIAGRAM OF CIRCULATORY SYSTEM

Fig.1- Block Diagram of Blood Circulatory System through Heart [ Figure is taken from Human Anatomy and Physiology : 2nd Edition by James E. Cronch ] IV. MEASUREMENT OF HEART RATE Now to measure the Heart Rate [ 4 ], a special type of frequency to voltage converter is designed. The measurement of average Heart Rate is seemed as diode pump circuit ( shown in Fig.4 ) which is combined of series connection of R1C1 network with two reverse direction connected diode one of which a parallel combined of C2R2 network connected.

Fig.2- Block Diagram of an average Heart Rate Monitor [ Figure is taken from Handbook of Biomedical Instrumentation : 21 st reprint 2002 ISBN 0-07-451725-2 By R S Khandpur ] Now, to obtain the frequency from diode pump circuit we generally follows the following formula : The average current over a period ‘t’ would be , N  CV  ……………. (2) i  Q   CVf av

The output voltage between two resistance R1 and R2 is proportional to the frequency i.e inversely proportional to the time (shown in fig.3 below). If the Heart Rate becomes lowere and the pulse does not appear across R at the next particular time, the output starts in fall and finally adjust the new value.

Fig.3- Principle of Frequency to Voltage Conversion

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Nirmalya Chandra, American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), June-August, 2014, pp. 139-143

[ Figure is taken from Handbook of Biomedical Instrumentation : 21 st reprint 2002 ISBN 0-07-451725-2 By R S Khandpur ]

Fig.4- Diode Pump Circuit [ Figure is taken from Handbook of Biomedical Instrumentation : 21 st reprint 2002 ISBN 0-07-451725-2 By R S Khandpur ] V.

NEURAL NETWORK FORMATION IN WORKING FUNCTION OF HEART

On the basis of Block Diagram ( shown in Fig.1 ) it is seen that multinetwork formation in the basis of veins and aorta. When these networks are represented as a controllers or as systems models, it becomes relatively easy to perform a closed loop analysis of a control system in terms of stability and convergence. Following Fig.5(a) shows a formation of Neural Networks

Fig.5(a) – An ‘L’ Layered Multi Input-Output Neural Networks Comparing this network with blood flow circulatory system throughout the human body, the following diagram of Fig.5(b) is created.

Fig.5(b) – Blood Flow system through Human Body from Heart Now, the simplest form of an artificial neural network is called perception which is first developed by Rosenblatt in 1958 [ 5 ] VI. PRINCIPLE OF ARTIFICIAL NEURAL NETWORK Suppose a function j=f (i) can be approximated by neural network. If the no of pattern available is ‘N’, then the cost function whose minimization is our moto, can be defined as : k N

E   Ek

(3)

k 1

where, Ek =

1 d jk ~ jk 2

(4)

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Nirmalya Chandra, American International Journal of Research in Science, Technology, Engineering & Mathematics, 7(2), June-August, 2014, pp. 139-143

‘

jk ’ is the kth actual pattern and ‘ jkd ’ is the kth desired pattern

Now the all pattern must have a weight factor i.e ‘w’. The minimization process of cost function is basically the principle of Gradient Descent by which the weight factor ‘w’ should be updated by the following process.

wnew  wold ~ 



E w

(5)

is Learning Rate and E should be minimum when w = wmin

Fig.6 – Weighted updated graph by the Principle Gradient Descent Algorithm From Fig.6 ,- when w < wmin

E w

is negative and

for that change in ‘w’ moves towards ‘wmin’ to be positive value.

When w > wmin vice versa is happened. In both cases, the slopes of w wmin depends on ‘ If,

w  wnew  wold , then w  - 



’

E w

(6)

This w is called Batch Update. VII. PSEUDO – INVERSE APPROACH The Pseudo –Inverse Approach computes the ‘w’ in batch mode. The weight factor ‘w’ represents the basis function in terms of vector signal representation. The width of the basis function is determined by :

 t

(7)

where ‘t’ is the maximum distance gap of centers and ‘f’ is the number of centers. Now, as per proportional rule if the basis function behaves as a constant, then the weight factor relates as, -

jk   k w T

where ,

n k  exp(  f

(8) 2

i k ~ cn )

(9)

Now, using these expression, overall pattern is represented as matrix form.

.w  j 

(10)

is the nth row vector and Transpose of

j  j1 j2 ..... jn



can be uses to represents the Transpose of output

j (11)

1

So using eqn (10) and eqn (11),

T T w   .   .  . j  

(12)

The R.H.S of eqn (12) is called ‘ Pseudo –Inverse ’ VIII. ELECTROCONDUCTION SYSTEM OFHEART : EQUIVALENCE FUNCTION OF MULTI NEURAL NETWORK The Conduction of Heart basically combined of  Sinoatrial ( SA) Node  Bundle of His  Atrioventricular ( AV) Node  The Bundle Branches

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 Purkinje fibers The SA nodes, located on the wall of right atrium in Heart provides a spark signal as a pulse by firing on its own but controlled by CNS to adjust the heart rate. In between SA node and AV node the band of specialized tissue is situated in which the velocity of propagation is faster than it is in atrial tissue. Blood in the atria is forced by the contraction through the values to the ventricles. Now, ventricles do not respond earlier than the atrial are empty of their contents. A delay line is needed to avoid any undesired thing in the case of ventricles response. This delay line is a function of AV node Now, if the Heart is blocked, the internal system ( electro-conduction ) of heart is interrupted. This undesired phenomenon can be treated as a cost function in neural network. To minimize these function there have to follow Principle Of Gradient Descent algorithm. When the SA node and AV node acts as a weightage factor equivalence to ‘w’ in neural network. According to this algorithm,

E E j    j d ~ j . n wn j wn

(a)

(b)

: The Central Function of Heart is denoted by

E ( ) cn ( ) E ( ) E j n (c) : Now,    cn ( ) j n cn n zn d = - j ~ j wn   zn cn zn z where,   n 2 .n  cn cn (  1)  cn ( )   1

(13)

cn ( ) mathematicaaly. The upgradation of cn ( ) is (14)

(15)

(16)

where,  acts as a basis function as discussed earlier in section VII The eqn (16) is the equivalence eqn of Least Mean Square Algorithm uses to minimization the quantized error in pulse samples. IX. CONCLUSION One motto in this paper is to give an idea for approach a technique of blood flow circulatory system in human body on the basis of Heart Pumping which quite equivalence of working process in artificial neural network. From the algorithm I try to create demands the minimization of possibilities any interrupted error in forms of Heart Problems. The details about active of Heart in any situation is under research. I basically try to give an aspect of continuity of blood flow by activation of Heart Function around the long life in our system. ACKNOWLEDGMENTS The author would like to thank the authorities of Birbhum Institute of Engineering and Technology for providing every kind of supports and encouragement during the working process. The author thanks to reviewers for giving us such attention and time. The author also acknowledge the unknown referees for their valuable comments and suggestions for improvement. Last but not the least the authors are giving a vote of thanks to our nearest and dearest parents and our be loving family members for providing mentally support to us. References : [1] [2] [3] [4] [5] [6] [7]

Introduction to Biomedical Eqipment : 4th Edition ISBN 81-7758-883-4 By Joseph J.Carr and John M.Brown Human Anatomy and Physiology : 2nd Edition by James E. Cronch Biophysical Measurements, Measurement Concepts Series Tektronix, Inc ( Beavertown, Ore.,1970) by Strong and Peter Handbook of Biomedical Instrumentation : 21st reprint 2002 ISBN 0-07-451725-2 By R S Khandpur F.Rosenblatt. The Perceptron : A Probabilistic model for information storage and organization in the brain.Psychological Review R.P.Lippmann. An Introduction to Computing with Neural Networks. IEEE ASSP Magazine, April 1987 Control of Nonlinear Dynamic Systems: Theory and Applications: J. K. Hedrick and A. Girard © 2010

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Estimation of radiation dose due to uranium in water to the public in Chamarajanagar district, Karnataka State, India K.M. Nagaraju, M.S. Chandrashekara*, K. S. Pruthvi Rani and L. Paramesh Department of Studies in Physics University of Mysore Manasagangotri, Mysore -570 006 INDIA Abstract: Uranium is the fundamental energy mineral of the nuclear power programme. It is a radioactive element, which delivers some quantity of radiation dose to the public. Uranium dissolves in oxygen-rich water that accounts for its presence in surface and ground water. Among its three isotopes of uranium, 238U is of more abundance in nature. Uranium and its progenies are expected to be in various matrices such as water, food, air, soil and rocks. Higher concentrations of uranium in water and food may leads to health risk in human beings. Estimation of uranium in water is essential, in order to assess the health risk from uranium in water. Therefore, activity concentrations of uranium in water samples in some locations of Chamarajanagar district, in Karnataka state of India were made, using the method of fluorimetry. This paper presents the variation in concentration of uranium, in water samples, from different locations of Chamaraja Nagar district, of Karnataka state, India. The concentration of uranium in water samples varies from, 0.75-115.66mBqL-1, with a median of 13.67mBqL-1. The annual ingestion dose due to 238U, is 0.057.598µSvy-1, with a median of 0.9µSvy-1. Keywords: Uranium; Water; Fluorimeter; Chamaraja Nagar. I. Introduction Exposure of man to natural radionuclides occurs through water, food and inhalation of air. The quantity of radiation dose received from natural sources varies from location to location, depending on the radioactivity content in air, water, rock, soil, and building materials [1]. According to the United Nations Scientific Committee on the Effect of Atomic Radiation, exposure to natural sources contributes more than 98% of the radiation dose to the general public[2,3]. The world average of human exposure from natural sources is 2.4mSv.y-1. Uranium (238U) is a naturally occurring heavy element, with an half life of 4.5×109 years, and is interest of study, due to its high abundance in nature. It occurs in minerals, monazite sands, lignite, phosphate rocks and fertilizers. It under goes a series of decay until it reaches the final stable product lead (206Pb). Uranium enters into the human body through drinking (ingestion) of water. Estimation of uranium concentration in water serves in two important purposes; hydrogeochemical prospecting of uranium and an assessment of health risk from uranium [4-6]. Some experimental evidences have shown that foodstuffs contributes nearly 15% of ingested uranium, and drinking water contributes about 85% [7,8]. The experimental evidence suggests that exposures to uranium may affect the respiratory and reproductive systems [9]. The main target of toxicity of uranium is kidney and lungs. Long term ingestion of uranium may results in an increase in the risk of kidney damage, cancer, and cardiovascular diseases [2,10]. An exposure of about 0.1mgkg-1 of body weight soluble natural uranium, in water causes transient damage to kidney [11]. United States Environmental Protection Agency, has recommended the maximum contamination level of uranium as zero tolerance only the safest limit [12,13]. World health organization recommended a reference level of 15μgl-1 [9]. As per US EPA the recommended safe level of uranium in water is 30 μgl-1 [13]. In the present study, an effort is made to study the activity concentration of uranium, in water samples at some locations in Chamaraja Nagar district, of Karnataka State, India. II. Geology of the Study Area The district is located in the southern tip of Karnataka state and lies between North latitude 11º 40' 58" and 12º 6' 32" and East longitude 76º 24¹ 14" and 77º 64' 55" It has an average elevation of 662m. Topography is undulating and mountainous with north south trending hill ranges of Eastern Ghats. Salem and Coimbatore districts of Tamilnadu in the East, Mandya and Bangalore districts in the North parts of Mysore district in the west and Nilgiris district of Tamilnadu in the south, bound the Chamaraja Nagara District. Chamaraja Nagar, Yelanduru, Kollegala, and Gundlupet are the taluks of the district. The district is endowed with rich both metallic and nonmetallic mineral sources. The major water supplies are boreholes for domestic and other purposes.

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Figure 1 Geology of Chamaraja Nagar District

III. Title Materials and methods A. Estimation of uranium in water by the method of fluorometry About 25 drinking water samples (20 litre at each location) were collected in plastic cans, from borewells, from different locations of the study area. The samples are then labeled, denoting the details of time, place and date of sampling. The concentration level of uranium, were determined by the method of fluorometry. The amount of uranium present in the water sample is estimated by using the equations 1 and 2, respectively.

Sample Counts 

Sample  Background   Background  Counts for 6 ppb 238U standard  Background   6

Uranium concentrat ion in the sample 

.........1

Samle Counts  3 g/l ....2 Volume of sample taken for analysis

B. Dose due to 238U in water: Radiation dose due to intake of uranium through the drinking water pathway for different age groups was calculated using the ICRP (International Commission on Radiological Protection) dose coefficients and prescribed water intake rates for different age groups[14]. The water intake rates taken (in Ld-1) for, infants of 06, and 7-12 months old are 0.7 and 0.8 respectively. For the children of age groups 1-3, and 4-8 years, 1.3 and 1.7 respectively. For 9-13 years, male chindlren 2.4 and 2.1for female children. For teenagers (age group 14-18 years), 3.3 for male teens and 2.3 for female teens and above 18 years (adults) it is taken as 3.7 (for male) and 2.7 (for females), is taken. ICRP-1996 is used. The annual radiation ingestion dose due to uranium intake through the drinking water pathway was calculated by using the equation 3.

A.D.( Sv/y)  MMCU(Bq/l) IW(l/y)  DCF(Sv/Bq) ....3

Where, AD is annual dose due to uranium (μSv.y-1), MMCU is measured mass concentration of uranium in water (Bql-1), IW is Intake of water(ly-1), DCF is dose conversion factor (μSv.Bq-1). IV. Results and Discussion Estimation of uranium concentration in water samples were made by the method of fluorometry, using LED Fluorimeter. The concentration of uranium in water samples and corresponding annual ingestion dose due to uranium in water were calculated and shown in Table.I. The concentration of uranium in water samples of the study area varies from, 0.75-115.66mBqL-1, with a median of 13.67mBqL-1. The annual ingestion dose due to 238 U, is 0.05-7.598µSvy-1, with a median of 0.9µSvy-1. Various health and environmental protection agencies have recommended some limit for uranium in drinking water for human being. World health organization has recommended 15μgl-1 in water is the safest limit [9]. United States Environmental Protection Agency suggests maximum contamination level of uranium in water as 30μgl-1[11,13]. Further UNSCEAR and ICRP have recommended the safe limits of uranium in drinking water as 9 μgl-1and 1.9 μgl-1 respectively[2,15]. The activity concentration of uranium in some locations of the study area such as Malai Mahadeshwara (MM) Hill, Uthamballi, Kunthuru, and Alur villages are found to be higher than the recommended value of 1.9 μgl -1 as per ICRP, 1979. Since uranium is a natural lithophilic element and it is present in all natural waters. The activity

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concentration of uranium depends on lithology, geomorphology, and other geological conditions of the study area [16]. Table I Concentration of uranium and dose due to uranium in water samples at different locations in Chamarajanagar district. Sl.No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Location

MM Hills Kowdalli Uthamballi Hanuru Kunthuru Mangala.K.T Madhuvinahalli Doddinduvadi Kollegala town Aluru Mangala.C.T. Badanaguppe Hebbasuru Panyadahundi Bendaravadi Range Average Median

238

U Conc. (μgl-1)

4.626 1.439 4.410 0.547 3.820 0.770 0.380 0.330 0.081 3.344 0.340 0.692 0.293 0.0436 0.03 0.03-4.63 1.41 0.55

238

U Conc. (mBql-1)

115.66 35.98 110.25 13.67 95.5 19.25 9.50 8.25 2.02 83.61 8.50 17.31 7.32 1.09 0.75 0.75-115.66 35.24 13.67

Annual Ingestion dose due to 238U (μSv y-1) 7.598 2.363 7.243 0.898 6.274 1.264 0.624 0.542 0.132 5.493 0.279 1.137 0.48 0.071 0.05 0.05-7.598 2.476 0.9

Higher concentrations of uranium observed in the water samples, may be due to ground water may contains traces of natural radioactivity related to uranium and thorium in soils and rocks. The higher activity concentrations of uranium in water samples may also due to traces of associated uranium mineralization and the geology of the study area. The southern and eastern portions of Kollegala taluk consists of lofty hills like MM Hills (which includes 77 hill ranges). Dodda Sampige hill range extended about 6km from north to south, which comprises of pale pink and gray granite. Due to presence of radioactive rich granites in these regions, the activity concentration of uranium in ground water samples of MM Hill, Uthamballi, Kunthuru, may be high. 238U and 226Ra commonly migrated to sites along fracture surface that are close to rock water interfaces. In Alur, village, the concentration of uranium content in soil being high, which may leach in to the water due to this reason higher concentrations of uranium in water were observed. However only in 27% of the water samples, uranium content is higher than the recommended limit of 1.9μgl 1 (ICRP, 1979). But in remaining 73% of the water samples, in the study area are found to be well below the recommended limits according to UNSCEAR, WHO, USEPA, [2, 9, 13]. The effective dose due to uranium in water in the study area are well below the reference level of committed effective dose of 100μSv, recommended by WHO. Further, from the results the value observed in the study area is very much low compared to worldwide observed values, 0.37-75.3μgl-1, in France; 0.04-12146 μgl-1, in Finland; 0.043-48.6 μgl-1, in Germany; and 0.00856.63 μgl-1, in China. Comparison of range of the activity concentration of uranium in water in study area with different countries is shown in Table II. Based on the rate of water consumed, the range, average median and standard deviation of annual ingestion dose (μSv.y-1) due to uranium for different age group were calculated and is shown in Table III. Compared to females, the dose rate due to uranium in water is found to be high in males of age group 9-13, 1418years old, and >18years(adults). Table II Comparison of concentration of uranium in different countries. Location No.

Country

1 2 3 4 5 6 7 8 9 10 11 12

United States France Argentina Romania Turkey Italy Germany Central Australia Finland Jordan China India(Chamarajanagar)

Range of Uranium (μg/l) 0.012-3.08 0.18-37.2 0.04-11.0 0.02-1.48 0.24-17.65 0.02-5.2 0.02-24 >20 0.02-6000 0.04-1400 0.004-28 0.03-4.63

References UNSCEAR, 2000 UNSCEAR, 2000 Bomben et.al.,1996 UNSCEAR,2000 Kumru, 1995 UNSCEAR,2000 UNSCEAR, 2000 Hostetler et.al.,1998 UNSCEAR, 2000 Smith et.al.,2000 UNSCEAR, 2000 Present Work

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Table III Dose due uranium for different age groups. Stages of Life Infants Children

Teens Adults

Age Group 0-6 Months 7-12Months 1-3 Years 4-8 Years 9-13 Years 14-18 Years >18 Years

Males Females Males Females Males Females

Dose due to 238U (μSvy-1) Range Average Median 0.01-1.33 0.44 0.16 0.01-1.52 0.50 0.18 0.02-2.47 0.81 0.29 0.02-3.23 1.06 0.38 0.03-4.56 1.49 0.53 0.03-3.99 1.31 0.47 0.04-6.27 2.06 0.74 0.03-4.37 1.43 0.52 0.05-7.03 2.31 0.83 0.03-5.13 1.68 0.61

SD 0.49 0.56 0.91 1.19 1.68 1.47 2.31 1.61 2.59 1.89

V. Conclusion The concentration of uranium in water samples of the study area varies from, 0.75-115.66mBqL-1, with a median of 13.67mBqL-1. The annual ingestion dose due to 238U, is 0.05-7.6µSvy-1, with a median of 0.9µSvy-1. Based on the rate of water consumed, the ingestion dose (μSv.y-1) due to uranium for different age group was calculated. The dose rate due to uranium in water is found to be high in male children of age group 9-13years old, male teens of age 14-8years old and male adults (>18years) than the female children, female teens and female adults. The effective dose due to uranium in water in the study area are well below the reference level of committed effective dose of 100 μSv y-1 recommended by WHO. References [1]

Rana, B. K., Tripathi, R. M., Sahoo, S. K., Sethy, N. K., Sribastav, V. S., Shukla, A. K., & Puranik, V. D. (2010). Assessment of natural uranium and 226Ra concentration in ground water around the uranium mine at Narwapahar, Jharkhand, India and its radiological significance. Journal of radioanalytical and nuclear chemistry, 285(3), 711-717.

[2]

UNSCEAR, Ionizing Radiation: “Sources and Effects on Ionizing Radiation,” Report to the general assembly with scientific annex, United Nation, New York: 2000.

[3]

Shashikumar, T. S., Chandrashekara, M. S., Nagaiah, N., & Paramesh, L. (2009). Variations of radon and thoron concentrations in different types of dwellings in Mysore city, India. Radiation protection dosimetry, 133(1), 44-49.

[4]

Rajesh, B. M., Chandrashekara, M. S., Nagaraja, P., Chandrashekara, A., & Paramesh, L. (2014). Distribution of 226Ra and 222Rn in bore well and lake water of Mysore Taluk, Karnataka State, India. International Journal of Environmental Sciences, 4(4), 558-566.

[5]

Joga Singh, Harmanjit Singh, Surinder Singh, B.S. Bajwa (2009). Estimation of uranium and radon concentration in some drinking water samples of Upper Siwaliks, India,” Environ Monit Assess. 154, 15-22.

[6]

Sahoo, S. K., Mohapatra, S., Chakrabarty, A., Sumesh, C. G., Jha, V. N., Tripathi, R. M., & Puranik, V. D. (2009). Distribution of uranium in drinking water and associated age-dependent radiation dose in India. Radiation protection dosimetry,136(2), 108-113.

[7]

Cothern, R. C., & Lappenbusch, W. L. (1983). Occurrence of Uranium in Drinking Water in the US. Health Physics, 45(1), 89-99.

[8]

Joga Singh, Harmanjit Singh, Surinder Singh, B.S. Bajwa, ‘Estimation of uranium and radon concentration in some drinking water samples. Radiation Measurements,” 2008, Vol. 43, S523–S526.

[9]

WHO, (2004) “Guidelines for Drinking Water Quality,” Radiological aspects, Geneva: World Health Organization; 3, 1-494.

[10] Harmanjit Singh, Joga Singh, Surinder Singh and B S Bajwa, (2009). Uranium concentration in drinking water samples using the SSNTDs, Indian J. Phys. 83 (7), 1039-1044. [11] USEPA, (2000) United States Environmental Protection Agency. “Standard for uranium in drinking water,” 65 FR 76707. [12] Ningappa, C., Sannappa, J., Chandrashekara, M. S., & Paramesh, L. (2008). Concentrations of radon and its daughter products in and around Bangalore city. Radiation protection dosimetry, 130(4), 459-465. [13] USEPA, (2003) United States Environmental Protection Agency. “Current drinking water standards,” Ground water and Drinking water Protection Agency, 1-12. [14] International Commission on Radiological Protection, (1996). Age dependent doses to members of the Public from intake of radionuclides: part 5. Compilation of ingestion and inhalation doe coefficient, Oxford : Pergamon Press, Annals of the ICRP 26(1). ICRP Publication 72. [15] ICRP, International Commission on Radiological Protection, (1979). “Limits for intake radionuclides by workers,” Pergamon Press, Oxford, ICRP Publication 30. [16] Sandeep Kansal, Rohit Mehra, N.P. Singh, (2011). Uranium concentration in ground water samples belonging to some areas of Western Haryana, India using fission track registration technique, 3(8), 352-357. [17] Chandrashekara, M. S., Veda, S. M., & Paramesh, L. (2012). Studies on radiation dose due to radioactive elements present in ground water and soil samples around Mysore city, India. Radiation protection dosimetry, 149(3), 315-320. [18] Shashikumar, T. S., Chandrashekara, M. S., & Paramesh, L. (2011). Studies on radon in soil gas and natural radionuclides in soil, rock and ground water samples around Mysore city. International Journal of Environmental Sciences, 1(5), 786-797. [19] Groundwater Information Booklet, (2008). Government of India, Ministry of Water Resources, Central Ground Water Board, Chamaraja Nagar District, Karnataka, 1-24. [20] Groundwater Information Booklet, Government of India, Ministry of Water Resources, Central Ground Water Board, Chamaraja Nagar District, Karnataka, 2012. 1-36.

VI. Acknowledgments The author is thankful to Prof. P.Venkataramaiah, Former Vice-Chancellor, Kuvempu University, for his constant guidance and encouragement.

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RELIABILITY ANALYSIS OF A COMPLEX SYSTEM WITH REPAIR MACHINE AND CORRELATED FAILURE AND REPAIR TIMES Pawan Kumar and Neha Kumari Department of Statistics, University of Jammu, Jammu-180006, India Abstract: The present paper deals with the reliability analysis of two non â&#x20AC;&#x201C; identical unit parallel systems with correlated failure and repair times and Repair machine failure. Of the two non â&#x20AC;&#x201C; identical units, one is priority unit and other one is ordinary. The repair of failed units is done by Repair machine, while the failed Repair Machine is repaired by repairman. The repair machine is given preventive maintenance after a random period of operation except when both the units are in failure mode. The random period of operation after which the repair machine is given preventive maintenance and the time of completion of preventive maintenance are independent exponential variates whereas failure and repair times of both the units are correlated random variables having joint density as bivariate exponential The different measures of system effectiveness are obtained by using regenerative point technique. Keywords: Parallel system; Repair machine; Reliability; Availability; Busy period; Expected number of repairs. I. INTRODUCTION A huge amount of literature is available in the field of reliability theory on the analysis of two unit priority system models. Various authors including (1-4, 6) have analyzed two unit system models assuming failure and repair times of the units as independently distributed random variables. But it has been found in many practical situations that failure and repair times are correlated random variables. With this concept of correlated failure and repair times various authors including (1,3, 4) considered system models assuming bivariate exponential distribution of failure and repair times. Further in system models authors have assumed that the machine device is used for repairing the failed units remains good forever. But in real situations this assumption is practicable and the repair machine may also fail during its working process. In case of nuclear reactors and marine equipments, robots are used for repair purposes and a robot again being a machine may also fail while performing its intended task. The concept of repair machine was introduced by Gupta and Chaudhary (5) in a two unit cold standby system with independent failure and repair times. In the present study we investigate and analyze a two non-identical unit parallel system model with a repair machine having correlated failure and repair times of units. Initially both the units work and repair machine is in good condition. Repair machine is used to repair the failed unit but if during the repair of units, repair machine fails then the repair of failed unit is discontinued and repair machine is taken up for its repair, and after the repair of repair machine the repair of failed unit is done afresh. The repair machine is given preventive maintenance after a random period of operation except when both the units are in failure mode. The priority unit is give preference in repair over non-priority unit. The random period of operation after which the repair machine is given preventive maintenance and the time of completion of preventive maintenance are independent exponential variates whereas failure and repair times of both the units are correlated random variables having joint density of the form

Where, is modified Bessel function of type one and order zero and is defined as By using regenerative point technique the following measures of systems effectiveness are obtained: 1. Reliability of the system and Mean time to system failure. 2. Expected up time of the system (0,t) and in steady state. 3. Expected busy period of the repair machine and repairman. 4. Expected number of repairs by repair machine and repairman. 5. Net expected profit earned by the system in (0, t) and in steady state

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1. 2. 3. 4.

5. 6. 7. 8.

II. SYSTEM DESCRIPTION AND ASSUMPTIONS The system consists of two non –identical unit working in parallel form named as priority and nonpriority unit with a repair machine (R.M.). Initially both the units are working. Both the units having two modes – Normal (N) and Total failure (F). The Repair machine repairs a failed unit but during repair it can also fail. In such a situation, the repair of a failed unit is discontinued and the repairman starts the repair of R.M. as a single repairman is always available with system. After a random period of time preventive maintenance is given to R.M. but when both the units are failed then preventive maintenance is not given to R.M. The failure and repair times of both the priority and non-priority units are taken to be correlated random variables having bivariate exponential distribution. The failure and repair times of R.M. are taken to be exponential distribution with different parameters. The random period of operation after which preventive maintenance is given to R.M. and period of completion of preventive maintenance both are exponentially distributed random variables with different parameters. III. NOTATIONS AND SYMBOLS : Random variables representing failure time of priority unit/ non-priority unit.i=1,2 resp. : Random variables representing repair time of priority unit/ non-priority unit.i=1,2 resp. : Joint p.d.f of is :

Marginal p.d.f of

; ; : Conditional p.d.f of

given

; : Rate of giving preventive maintenance to Repair machine : Rate of completion of preventive maintenance : Failure rate of Repair machine : repair rate of Repair machine SYMBOLS FOR THE STATES OF THE SYSTEM / : Priority/non priority unit is in normal mode and operative : Priority/ non-Priority unit is under repair : Priority/ non-Priority unit is waiting for repair : Repair machine is operative/ good and non- functioning : Repair machine is under repair/ preventive maintenance. With the help of the above symbols the possible states of the system are: ,

FIG. 1. TRANSITION DIAGRAM

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IV. TRANSITION PROBABILITIES AND SOJOURN TIMES A. STEADY STATE PROBABILITIES: First we find the following conditional direct and indirect steady-state probabilities of transition:

Similarly,

Unconditional steady state probabilities of transition are:

, also,

Similarly,

(1- 26) It can be easily seen that the following results hold good:

(27-36) B. MEAN SOJOURN TIMES: The mean sojourn time in state denoted by is defined as the expected time taken by the system in state before transiting to any other state. To obtain mean sojourn time , in state , we observe that as long as the system is in state , there is no transition from to any other state. If denotes the sojourn time in state then mean sojourn time in state is: First we obtain the following conditional mean sojourn times:

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Unconditional mean sojourn times are given by

Also, Similarly, (37-47) V. ANALYSIS OF RELIABILITY AND MTSF Let the random variable be the time to system failure when system starts up from state reliability of the system is given by

, then the

Using the definition of relations among can be developed, taking their Laplace transforms and solving the resultant set of equations for we get (48) Where,

and

To get MTSF, we use the well known formula (49) Where,

and

Since we have VI. AVAILABILITY ANALYSIS Define as the probability that the system is up at epoch‘t’ when it initially starts from regenerative state . To obtain recurrence relations among pointwise availabilities we use the simple probabilistic arguments. Taking the Laplace transform and solving the resultant set of equations for , we have (50) Where,

(51) And

(52) Where,

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The steady state Availability will be given by Where

(53) Since ,by using L,Hospital rule we have Hence on using L’Hospital’s rule, becomes (54) Therefore,

Using the relation

we get (55)

Where,

using (53) and (55) in (54), we get the expression for . The expected up time of the system during (0, t] is given by So that, VII. BUSY PERIOD ANALYSIS BUSY PERIOD ANALYSIS OF REPAIRMAN/REPAIR MACHINE Define as the probability that the repairman/repair machine is busy in the repair of the failed repair machine /unit when the system initially starts from state . Using probabilistic arguments, the value of can be obtained in its L.T as: (56) Where,

(57) Where, In the steady state, the probability that the repairman will be busy is given by (58) Where,

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(59) Where, Similarly, (60) Where,

(61) Where, In the steady state, the probability that the repair machine will be busy is given by (62) Where,

(63) Where, The expected busy period of the repairman during (0, t] is given by So that, The expected busy period of the repair machine during (0, t] is given by So that, VIII. EXPECTED NUMBER OF REPAIRS EXPECTED NUMBER OF REPAIRS BY REPAIRMAN/REPAIR MACHINE Let us define as the expected number of repairs by repairman/ of repair machine during the time interval when the system initially starts from regenerative state .Using the definition of / the recursive relations among can be easily developed, using L.S.T. and solving for we get (64) Where,

(65) Where,

In the long run the expected number of repairs per unit of time by the repairman is given by (66) Where,

(67) Where,

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Similarly, (68) Where,

(69) Where, In the long run the expected number of repairs per unit of time by the repair machine is given by (70)

(71) Where, IX. PROFIT FUNCTION ANALYSIS Two profit functions and can easily be obtained for the system model under study with the help of characteristics obtained earlier. The expected total profits incurred during (0, t] are: Expected total revenue in (0,t] Expected total expenditure in (0,t] (72) Similarly, (73) Where, is revenue per unit up time. is the cost per unit time for which repair man is busy in repair of the Repair machine. is the cost per unit time for which Repair machine is busy in repair of the failed unit. is per unit repair cost. is repair cost for Repair machine. The expected total profits per unit time, in steady state, is

So that, (74) (75) X. GRAPHICAL STUDY OF THE SYSTEM MODEL For more concrete study of system behavior, we plot MTSF and Profit functions with respect to of priority unit) for different values of .

(failure rate

Fig. 2 shows the variations in MTSF in respect of for different values of as 0.25, 0.50 and 0.75 while the other parameters are fixed as =0.05 =0.8 =0.7 =0.05 =0.06 =0.4, =0.5 =0.50. It is observed from the graph that MTSF decreases with the increase in the failure parameter and increases with the increase in . Fig. 3 represents the change in profit function and w.r.t. for different values of as 0.25, 0.50 and 0.75 while the other parameters are fixed as =0.05 =0.8 =0.7 =0.05, =0.06 =0.4, =0.5 =0.50, =1000, =300, =250, =350, =200.

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From the graph it is seen that both the profit functions decrease with the increase in failure rate and increase with the increase in . It is also observed that profit function is always higher as compared to profit function for fixed values of and . Behaviour of MTSF w.r.t. λ 1 for different values of r1 240 220 r1=0.25 r1=0.50 r1=0.75

200 180

MTSF

160 140 120 100 80 60 40 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

λ1 Fig. 2

Behaviour of P1 & P2 w.r.t. λ 1 for different values of r1 300 280 r1=0.25 r1=0.50 r1=0.75 r1=0.25 r1=0.50 r1=0.75

260 240

P1 & P2

220 200 180 160 140 120

100

80 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

λ1 Fig. 3

[1] [2] [3] [4] [5] [6]

REFERENCES Goel, L.R. and P. Shrivastava (1991); A Two Unit Standby System with Imperfect Switch, Preventive Maintenance and Correlated Failures and Repairs. Microelectron Reliab., Vol.32, pp.1687-1691. Goel, L.R. and P. Gupta (1984); Analysis of Two Engine Aeroplane Model with Two Types of Failure and Preventive Maintenance. Microelectron Reliab., 24, 663-666. Gupta R., C.K. Goel, and A. Tomer (2010); A Two Dissimilar Unit Parallel System with Administrative Delay in Repair and Correlated Lifetimes. International Transaction in Mathematical Sciences and Computer, 3 (1), 103-112. Gupta R., P. Kumar and Shivakar (2003); A Two Unit Parallel System with Repair Machine Failure and Correlated Failure and Repair times. Gujrat Statistical review, Vol. 30, No. 1-2, pp. 19-34. Gupta R., Alka Chaudhary (1995);Stochastic Analysis of Priority Unit Standby System with Repair Machine Failure. International Journal of Systems Science, 26, 2435-2440. Osaki, S. and T. Nakagawa (1975); Stochastic Behaviour of Two Unit Priority Standby Redundant System with Imperfect Switchover, IEEE Trans. Reliab., Vol. R-24, 143-146.

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Antenna Utility for Revised Orientation towards Radio Waves Anmol Oberoi1 Rodney Lobo2 Veena Divya K.3 Department of Instrumentation Technology1,2, Assistant Professor, Department of Instrumentation Technology3 R. V. College of Engineering, V. T. U. Mysore Road, R. V. Vidyanikethan Post, Bangalore, Karnataka 560059, India

Abstract: A tracking system is used for observing or following objects on the move and supplying an ordered sequence of information, which in this case is the location data to a model, which is capable of providing display. As the name suggests, this tracking system is modelled using an antenna which is unidirectional, the YAGI-UDA. The antenna is designed so as to follow the motion of any air borne system with a frequency of 433 MHz. The GPS coordinates are received in a timely fashion in order to control the antenna direction and motion, so as to make it unidirectional and limits its use as an omnidirectional antenna. The tracking system uses a combination of mechanical devices in order to control to the movement of the antenna. The stepper motor is used to update the azimuth of the antenna and the servomotor is used to supply an update in the elevation relative to the airborne system. All of these mechanic motions are controlled with the help of the software control which consists of a microcontroller, which activates the drivers in order to run the mechanical motors in accordance with the change in the position of the airborne system. The system looks at incorporating modularity, portability and cost control keeping in mind the advances in technology and the limitations of the existing systems. Keywords: Antenna, Radio waves, Yagi-Uda, Tracking system, Revised Orientation, GPS. I. Introduction An antenna is a means of radiating or receiving radio waves. It is a metallic device, which may even be a rod or a wire. In other words an antenna is a transitional structure between free space and a guiding device. Certain air borne systems are designed for specific applications such as satellites for monitoring the climate conditions, satellites for telecommunication or for radio and television broadcasting, unmanned air vehicles for aerial security, aerial photography. Tracking of such systems becomes a matter of utmost concern due to their highest priority applications. While many methods already exist for tracking of airborne systems, most of these systems lack portability due to their titanic size. This system aims at overcoming the above mentioned limitation and also improving the execution, taking into consideration various parameters. The system finds its application in various verticals which begin from defence and continue over confidential and specific frames such as satellites and spaceships. This system uses a directional antenna, namely the Yagi-Uda antenna, which moves in the direction of the airborne system and hence all the power is supplied into a single direction. This design achieves a very substantial increase in antennaâ&#x20AC;&#x2122;s directionality and the gain compared to a simple dipole. Section I is an introduction on Antenna Tracking system and Section II makes it clearer by giving an insight on Anatomy of Tracking systems. The Section III throws light upon the existing Tracking systems. Design features are dealt with in section IV, which is followed by the methodology in the next section, consisting of proposed block diagram and its modules. II. Existing Technology GPS tracking came about using satellites to determine location or position to with a high accuracy. This became a boonfor the military, allowing its units to be monitored and controlled from a remote location. This depicted its application in a very transparent way. Eventually, GPS technology was made public. This allowed the common man to take advantage of the satellite network. The public now has the access to GPS devices to navigate andreachtheir destination in the shortest amount of time. While throwing some light upon the existing technology, one would never fail to mention some of its unmatched advantages which include extremely flawless and high range of tacking. But with such advantages, it includes limitations too which have been clearly noticed and eliminated in the current system under development. A few of these limitations are portability, modularity and not forgetting the extremely high cost of

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the system [1][3]. The system under consideration is built using different modules which is clearly depicted on the block diagram and hence it has positive stability The figure given below is a pictorial representation of what an existing system would look like, which very clearly shows they are not portable and as the system is very large, it increases its range enormously. While not all existing systems use a directional antenna for better directivity and gain control, the system under consideration uses a Yagi-Uda Antenna for enhanced directivity when compared to the existing system. These existing systems use omni directional antennas [2]. Omni directional antennas supply equal power in all directions, this limitation is overcome by using a directional antenna which supplies all the power in the direction of concern and suppresses the power in other directions [3]. Fig. 1 Tracking system

III. Anatomy of tracking [7] The functionality of the tracking systems are mentioned as follows: • The system is in the ready state at first. • As soon as an airborne system is located in its range of operation, the system starts to receive its GPS coordinates. • On receiving the GPS coordinates, the azimuth and elevation are decoded. • The software module updates the hardware module in accordance with the decoded values. • The antenna, which is controlled by the hardware module, follows the air borne system and collects data from it. IV. Design fetures of Yagi-Uda Antenna A Yagi-Uda antenna is a widely used antenna design due to its high forward gain capability, extremely low cost and ease of construction as such, not taking the design in to consideration. It is commonly used as a roof top television or radio receiver; it can also be used as a transmitter antenna based on the application. Basically an antenna is a real system that matches or coupled the energy to the free space. A Yagi-Uda antenna is a directional antenna system consisting of an array of dipole and additional closely coupled parasitic elements, which are reflectors and directors. The dipole element is directly connected to the transmission feed line and is responsible for energizing the entire structure. The reflector is 5% percent longer than the dipole and the dipole is 5% longer than the director element. The number of directors depends upon the gain and for achieving high gain it is better to have equally spaced elements with large number of directors. More directors mean better gain. The function of the parasitic element is to improve the radiation pattern in the forward direction. The reflector provides a 3db additional gain, but having one reflector provides little benefit. There are no simple design rules or formulas for designing a Yagi antenna due to nonlinear relationship between physical parameters such as element length, diameter and position and electrical characteristics such as input impedance and gain but performance can be estimated by computer simulation [11]. V. Modus Operandi A.The design and development of such a tracking system can be carried out in the following manner: • Design of antenna for 433MHz frequency range-The antenna is designed using QY4 simulation software. The radiation patterns for the designed antenna with 5 elements is depicted in the following pages. • Design of control system – The control system is the support module. Constructed using a structure to mount the antenna. The structure comprises of the stepper motor for relevant azimuth update and a

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servo for relevant elevation update. All updates take place in accordance with the GPS modules received from the airborne system. • Integration of modules- Various modules are integrated. These include the mounting of the antenna on to the control structure, whose movement is in turn controlled by a microcontroller, contained with a user fed program. The above procedure is followed by testing the system which includes- Transmission of the UHF signal, tracking of the airborne system and data presentation at the ground station in order to depict the elevation, angle and various other parameters associated with the airborne system B. Block diagram Fig. 2 Block Diagram representing the flow of signal once a tracking system is functioning.

C. Modules Fig. 3 Various modules of the antenna tracking system, with specific elements used under each module.

VI. Antenna Tracking The Yagi-Uda antenna is designed to have 5 elements. Three of which are reflectors. The antenna is designed using QY4 software. QY4 stands for quick yagi version 4, which is a software developed specifically for designing a YAGI UDA antenna. The software can help design antennas automatically or manually [10]. All the dimensions for a particular design frequency are judged with the help of a few parameters, namely the element diameter, operating frequency, number of directors and the spacing between each element. The Yagi-Uda antenna is designed for the following specifications: Frequency (MHz) Boom Length (m) Gain (db) Diameter of elememts (m) Front to Back ratio (db) Input impedance (Ω) Directivity (dB)

433 0.45 10.32 0.006 23.43 17.8 9.2

A. Range of length of different elements Length of reflector= 0.47 λ – 0.52λ Length of driven element= 0.45λ – 0.49λ

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Length of directors= 0.4λ – 0.45λ B. Range of spacing between different elements Spacing between reflector and driven element= 0.16 λ – 0.35λ. Spacing between driven element and nearest director= 0.2 λ - 03λ. Spacing between directors= 0.2 λ - 03λ. Range of diameter of elements = 0.001λ – 0.003λ. C. Directivity Calculation Directivity of N element YagiUda antenna= 10logN + 2.2dBd. For N=5, Directivity = 10log5 + 2.2 = 9.2dBd [13]. VII. UHF/VHF Range Calcultions [15] Range of an antenna can be determined by carefully observing various parameters, which must be calculated or determined with testing. The parameters are as specified below. A. Radio line-of-sight calculation The radio line of sight calculation determines the theoretical maximum range. The actual range is usually lesser than this, because of the variables in transmitter power, receiver sensitivity, line losses and antenna efficiency. The calculation is carried out by the formula specified below. D = 1.33(SQRT(2Hr) + SQRT(2Ht)) Where, D= distance to radio horizon(miles) Hr= height of RX antenna(feet) Ht= height of TX antenna(feet) B. Line losses The path followed by the Radio frequency energy, experiences a loss of power as it is sent to and from the antenna. The cause of this loss is imperfect shielding and due to reflection of energy through line couplers. Typical values for line loss of the most commonly used coaxial cable RG-58, is approximately 9.5 dB per 100 feet. C. Path loss The loss of power that occurs as the signal propagates through free space from the transmitter to the receiver is called path loss. PL= 117 + 20log(F) – 20log(Ht*Hr) + 40log(D) Where, PL = path loss in dBm. F = operating frequency in MHz. Ht, Hr = height of transmit and receive antennas( feet). D= distance between antennas(miles). D. Antenna Gain Antennas do not actually produce a gain as they are passive elements in an RF circuits. However they can produce an effective gain in a particular direction by focussing the energy in a specific plane or pattern. E. Impedance matching Antenna impedance relates the voltage to the current at the input to the antenna. If the impedance is a real number, then the voltage is in phase with the current. If the impedance is given by a complex number, then imaginary numbers give phase information. Maximum transfer of RF at the design frequency occurs when the impedance of the feed point is in accordance with the impedance of the feed line. This is known as impedance matching.

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VIII. Radiation Patterns A. Azimuth Fig. 4 Linear radiation pattern

Fig. 5 Logarithmic radiation pattern

B. Elevation Fig. 6 Linear radiation pattern

Fig.7 Logarithmic radiation pattern

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C. Gain Fig. 8 Pattern for Gain, Front to back ratio and voltage standing wave ratio.

The antenna radiation patterns are sometimes known as the polar plots and play a major role in the overall performance of the yagi antenna. A plot of gain vs direction is called the radiation pattern. On increasing the number of directors within a given boom length, does not result in a better gain but gives a better control of the antenna â&#x20AC;&#x2DC;s pattern of a wider range of frequencies in the band of the design. By reducing the length of the directors and increasing the spacing between the directors results in a very clean pattern with good pattern bandwidth. IX. Representation of Antenna Constructed for 433 MHz Frequency Fig. 9 Yagi Antenna at 433MHz

X. Hardware Breakdown The hardware vertical of this tracking system concentrates as two major players of the antenna position and motion control, which are the stepper motor and the servomotor [14]. The stepper motor provides the necessary changes in the azimuth in accordance with the current positioning of the system to be tracked. This updation takes place on every received GPS co ordinate value. The servomotor contributes to the elevation change in accordance with the GPS co ordinates. The functioning of these two components in relation to each other depict the control system of this tracking system [16]. The system considerations are defined as follows. Stepper motor provides a means of accurate positioning and speed control without the use of any feedback controllers or sensors. The stepper motor considered is a 5kg torque motor, as it needs to vary the azimuth of an antenna of a considerable weight. Figure 10 shows the physical properties of the model developed for tracking. The structure is a wooden crafted one, with two supporting pillars and a base. The supporting pillars are 46cm long each , with a width of 3cms. The pillars consist of a rotating base of dimensions 34, 17, 0.75. The square shape base has each side of 50cm. The rotating base consists of a shaft, which is a brass rod of 6mm. The antenna is mounted on the rotating base and the servo motor is installed onto one of the pillars for a motion of 0

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to 180 degrees. The servo motor is a very important aspect as far as tracking is concerned, as it controls the elevation of the entire system and it must be strong enough to elevate the entire system. Keeping this in mind a servo motor of torque 9kg-cm was chosen as this could lift the complete weight of the base carrying the stepper motor and the antenna, both mounted on a base of over 1kg [4]. Fig.10 Physical properties of the structure developed for mounting the Yagi Antenna

GPS Module analysis Transmitter: Working voltage: 3V - 12V for maximum power use 12V. Working current: maximum less than 40mA and minimum 9mA. Working frequency: 315MHz or 433MHz. Transmission power: 25mW (315MHz at 12V). So this module transmits up to 90m in open area. Receiver: Working voltage: 5.0VDC +0.5V. Working current: ≤5.5mA maximum. Working frequency: 315MHz-433.92MHz. Bandwidth: 2MHz The use of an optional antenna will increase the effectiveness of the wireless communication. XI. Control System Design Fig. 11 Free body diagram of rotating structure [12]

In the above free body diagram (FBD), J1, J2 – Moments of inertia of rotating table and antenna respectively B1, B2 – Coefficients of rolling friction of the shafts of rotating table and stepper respectively T – Torque applied using the servo θ1 – Intermediate rotation θ – Rotational output at the antenna Consider the moment of inertia J1 which has the applied torque T. the opposing torque due to friction and moment of inertia are, TB1 and TJ1. From Newton’s second law for rotational motion,

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The FBD for J1 can be drawn as such,

(a) (b) Taking Laplace Transform with zero initial conditions (c) (d) Consider the moment of inertia J2 which has the applied rotation θ1. The opposing torque due to friction and moment of inertia are, TB2 and TJ2. The opposing torque due to stiffness is TK. The FBD for J2 can be drawn as such,

From Newton’s second law for rotational motion, (e) (f) Taking Laplace Transform with zero initial conditions (g) Rearranging the terms, (h) Substituting (8) in (4) (i) Hence the transfer function is, (j)

The various constants are found using simulations and calculations done by using the values of dimensions of the structures and they are listed as follows: J1 - 0.08 kg.m2 J2 - 0.02 kg.m2 B1 - 0.6 B2 - 0.2 The final form of the transfer function after substituting the values is:

(k)

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XII. Results Table 1. Antenna UHF/VHF Range Calculations at 433MHz frequency: Range Parameter Value Radio Line of Sight 13.5251 Miles Line losses (Due to coax) 0.095 dB/ft Antenna Gain 10.26 dB Path Loss 46.3781 dBm Height of transmitter= 33.75 ft Height of receiver=3.375 ft Frequency= 433MHz D=0.0088 miles Fig.12 Screenshot of the system developed with integration of various modules

Table 2. GPS Coordinate values received in terms of degrees and converted to linear values for the Antenna control (In accordance with Airborne system): Degree values Vyoma workshop at RVCE

Linear values Vyoma workshop at RVCE

Lat - 12.922490

Lat – 110629.8016

Lon - 77.499359

Lon – 108518.3237

Parking lot at RVCE

Avg. longitude – 108319.1906 Parking lot at RVCE

Lat - 12.922608

Lat – 110629.8026

Lon - 77.499359

Lon – 108516.7733

Amphitheatre at RVCE

Avg. longitude – 108319.1394 Amphitheatre at RVCE

Lat - 12.922049

Lat – 110629.7979 Lon – 108518.5141

Lon - 77.498118

Avg. Longitude – 108319.3819

XIII.Conclusion The performance of this system depends upon the antenna design. The antenna design is one the most crucial aspect of the system as it decides not only the specification of other parameters related to the system but also contributes to the tracking of the airborne system of a particular frequency range. Hence the antenna is modelled with the right set of dimensions to ensure that it functions for the exact same frequency range as that of the airborne system. In this paper, the methodology to be followed in order to design the entire system which is range independent is discussed. The antenna performance is analysed by the appropriate simulations.

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References [1] [2]

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

eitz J., Vaupel T., Thielecke J., ‘Wi-Fi azimuth and position tracking: Signal propagation, modeling and evaluation’, Published in: Information Fusion (FUSION), 2013 16th IEEE International Conference, 1479 – 1486,9-12 July 2013. Zhefeng Sun, Huan Li, ‘Using Directional Antenna for Continuous Moving Object Tracking in WSN with Uncovered Holes’,Published in: Distributed Computing Systems Workshops (ICDCSW), 2013 IEEE 33rd International Conference, 274 – 279, 8-11 July 2013. Tserenlkham B., Batdalai S., ‘Antenna tracking system for broadband portable terminal’,Published in: Strategic Technology (IFOST), 2013 8th International IEEE Forum(Volume:2 ),159 – 162, June 28 2013-July 1 2013. Zhong-Ke Shi, ‘Real-time learning control method and its application to AC-servomotor control’, Published in: Machine Learning and Cybernetics, 2013. Proceedings. 2013 International Conference , 900 – 905, Date of Conference:2013. Jium-Ming Lin and Po-Kuang Chang’Intelligent Mobile Satellite Antenna Tracking System Design’. Published in: SICE Annual Conference 2008. Date of conference : 2008. T.-Y. Shih, C.-L. Li, and C.-S. Lai, “Design of an UWB fully planarquasielliptic monopole antenna,” presented at the Proc. Int. Conf. ElectromagneticApplications and Compatibility (ICEMAC 2004), Taipei,Taiwan, Oct. 14–16, 2004. T. He, C. Huang, B. Lum, J. Stankovic, and T.Adelzaher, “Range- free localization schemes for largescale sensor networks,” in Proc. ACM MobiCom, SanDiego, CA, pp. 81–95, Sept. 2003. H. G. Schantz and L. Fullerton, “The diamond dipole: a Gaussian impulse antenna,” presented at the IEEE Antennas and Propagation Soc.Symp., Jul. 8–13, 2001. S. D. Targonski and D. M. Pozar, “Aperture-coupled microstrip antennasusing reflector elements for wireless communications,” in Proc. IEEE-APS Conf. Antennas and Propagtion for Wireless Communications,Nov. 1998, pp. 163–166. X. H. Wu and Z. N. Chen, “Design and optimization of UWB antennasby a powerful CAD tool: PULSE KIT,” presented at the IEEE Antennasand Propag. Society Symp., Jun. 20–25, 2004. Balanis, ‘Antenna theory: Design and Analysis’, John Wiley & Sons Publications, 2nd Edition, 2007. A. NAGOOR KANI, ‘ Control systems theory’, RBA Publications, 2nd Edition, 2011. J. D. Jackson, ‘Classical Electrodynamics’, 2nd Edition, 2011. C. L. Wadhwa, ‘Basic Electrical engineering’, New Age International Publishers, 2nd edition, 2006. ‘145-1993 - IEEE Standard Definitions of Terms for Antennas’. Page(s): 1 – 32. Date of Publication : April 2013. Norman S. Nise, ‘Control Systems Engineering’ , John Wiley & Sons Publications, 6th Edition, 2010. Jacqueline Wilkie, Michale Johnson, Reza Katebi , ‘Control Engineering an Introductory Course’, Publisher: McGraw Hill, Edition: 2002.

Acknowledgement The authors of this paper would like to thank all their peers for their valuable, unmatched information and survey statistics without which it would have been hard to bring together the contents of this paper.

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Efficient Crawling in Online Social Networks Using Metro-polis Hastings Random Walk Technique 1

Kulvinder Singh, 2Sanjeev Dhawan, 3Kirti Saini 1,2 Faculty of Computer Science & Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra-136119, Haryana, INDIA 3 M.Tech. (Computer Engineering) Research Scholar University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra-136119, Haryana, INDIA. Abstract: The immense interest generated by OSNs (Online Social networks) [1] has given rise to a number of measurement and characterization studies that attempt to provide a first step towards their understanding. Unlike the Web, which is largely organized around content, online social networks are organized around users. Participating users join a network, publish their profile and any content, and create links to any other users with whom they associate. Sampling techniques are essential for practical estimation of OSN properties. While sampling can, in principle, allow precise population-level inference from a relatively small number of observations, this depends critically on the ability to draw a sample with known statistical properties. The lack of a sampling frame (i.e., a complete list of users, from which individuals can be directly sampled) for most OSNs makes principled sampling especially difficult. To elide this limitation, our work focuses on sampling methods that are based on crawling of friendship relations - a fundamental primitive in any OSN. As online social network is very wide and densely connected, so it is very difficult to crawl a network. Many crawling techniques are there having their own cons and pros. In this paper, MHRW is used for crawling a social network. MHRW (Metro-polis Hastings Random Walk) technique is a crawling technique which is an enhanced technique of RW (Random walk). In RW node is selected randomly and so it is biased towards higher degree nodes and this limitation is overcome in MHRW. In MHRW, node is selected on probability basis. The node which is having more number of edges connected to it have more probability of being chosen and the node with less number of edges have less probability. Node with higher probability is selected as next node for crawling. Keywords: OSN, MHRW, RW, degree bias, nodes 1.

Introduction

MHRW [2] is the node sampling method and is employed to get unbiased samples in undirected social graphs i.e keeping the node degree distribution of the original graph unchanged. The process of sampling a graph is usually starts from one single seed or multiple seeds. After the nodes have been sampled, the knowledge of the nodeâ&#x20AC;&#x2122;s in edges and out edges can be used to choose the next node. The policy of choosing the next node depends on the design of sampling algorithms. Here MHRW is implemented. In MHRW, a proposal function is designed based on the probability distribution. By randomly accepting or refusing the proposal, the proposal function changes the transition probabilities which makes the samples converge to the probability distribution. For implementation Java is used in front end and for database oracle is used as back end. Code is generated for implementing MHRW for selecting the nodes for crawling. In this a scale is created for putting the node in different range, a node with higher probability has the maximum chances of probability that maximum number of nodes are being chosen from them and the one with lower probability has chances of less nodes are selected. The scale is divided into 10 partitions for putting the value in different range. The starting range of scale contains maximum number of nodes range and as the scale reaches to the end the number of nodes becomes very less. The technique is implemented for undirected network which is bidirectional. This means that if a user is in his/her friend list then the user also contains that friend in his list also. This means that if a node 1 is connected to node 2 then node 2 is also connected to node 1. For example if Kirti is in friend list of Priya then Priya must be in friend list of Kirti and they are connected to each other. The value of the node is computed by how densely it is connected to network. By applying formula if the value comes in between 0 and 1 then the node is added to the list otherwise it is rejected. The probability of selecting a node is maximum in the beginning of scale and as

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it reaches to an end nodes selected are very less. And at the end, the nodes are collected in different partitions of the scale giving the probabilities on the basis of which nodes are selected for crawling. For implementing this technique Java is used with NetBeans IDE 7.3 and Oracle is used as the back end database. Different classes were introduced for nodes, edges, for selecting nodes and for database connectivity. II. Related Work There were many techniques studied for crawling by researchers and discussed their cons and pros. In the literature survey different techniques were studied and one of the technique MHRW is selected for implementation as it produced unbiased results. The results of BFS are variant and they produced samples that are not only biased but also do not have any statistical proof which means that their results are not accurate and cannot be trusted. So it is the poorest method but it is good if the network is simple and small and is widely used techniques. FS samples edge uniformly, which indicates that the sample set is biased towards higher degree vertices. Unlike, MHRW which aims at obtaining an unbiased sample directly, Frontier Sampling (FS) corrects the bias by specific estimators. Junjie et al. [3] focused on sampling the directed networks and intended to compare the efficiency, the accuracy and the stability between them. It was considered, the sampled nodes and links as a whole and separated from the original one. Experiments were evaluated by deploying the snow ball method, the random walk method, DMHRW and MUSDSG with different sampling ratios on the datasets. Yuming Mai [4] surveyed about the use and performance of the Metropolis-Hasting Random Walk (MHRW) and other algorithms to obtain uniform samples from the facebook and other online social networks (OSNs). The survey was composed of two subsections: Firstly, he compared MHRW with one algorithm and the other was comparing MHRW with more than one algorithms and then described recent research efforts to improve the efficiency and accuracy of MHRW and their results. If RWRW is treated as an edge sampling method, RWRW uses exactly the same estimators as FS. The comparison between MHRW and RWRW are also suitable for comparing MHRW and FS. Firstly, MHRW has the â&#x20AC;&#x153;ready to useâ&#x20AC;? merits, since vertices are sampled uniformly. However, FS is biased towards the high-degree vertices and requires re-weighing appropriately. Secondly, specific estimators should be built for estimating different graph properties. However, only estimators of degree distribution and global clustering coefficients are currently available and estimators for purely data-analytic procedures, such as hierarchical clustering or multi-dimensional scaling are impossible to be constructed. Thus MHRW is more simple and versatile than FS in practice. We can conclude that the BFS, RW and RJ do not converge to uniform sampling and are biased towards the high degree-vertices. Further improvements or variants of random walks include random walk with jumps, multiple dependent random walks, weighted random walks, or multigraph sampling [5]. Rasti et al. [6] made an attempt to sample unstructured point to point overlays. They compared Metropolized Random Walk with their own developed RDS. Initially both the sampling techniques were tested using various static and dynamic graphs where the distribution of the sampled property is known exactly. Then both techniques were evaluated using Gnutella overlay and finally compared the samples and true distribution using Kolmogorov-Smirnov (KS) basis. Rasti et al. claimed that RDS performed significantly better than MRW when the overlay structure represent highly skewed node degree and highly skewed node clustering coefficients combination. Wang et al [7] applied MHRW and get the first unbiased sample of directed social graph. Thesis by Alan [8], used novel measurement techniques to study online social networks at scale, and used the resulting insights to design innovative new information systems. RW sampling, bias can be quantified by Markov Chain analysis and corrected by appropriate re-weighting (RWRW). MHRW samples vertices uniformly; however, one design assumption of MHRW is that the social graph is well connected. If efficiency is required then RWRW is better and if simplicity is the requirement then MHRW is the better choice. III. Implementation MHRW is one of the efficient techniques for crawling as it does not produced biased results for higher degree nodes. Our focus is to crawl a network more efficiently in less time. First of all we select the number of nodes for which we want to create a network for crawling by MHRW. Now for each node, the numbers of edges connected to it are calculated. As MHRW crawl network on probability basis. Diff is calculated by subtracting the maximum value from the minimum value and dividing the output with the 9. diff= While d is calculated by the formula

where val is the random value selected to find its probability.

Random. Math is used to compare the coming values. Random.math always contains value between 0 and 1. Random.math <

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Enter no. of nodes to work for. 100 final friend count list is [7, 2, 1, 14, 7, 10, 8, 8, 8, 8, 6, 7, 3, 4, 5, 7, 10, 11, 5, 4, 6, 3, 5, 9, 6, 8, 3, 9, 5, 8, 4, 7, 7, 10, 8, 11, 3, 13, 11, 5, 9, 7, 4, 4, 6, 8, 7, 8, 5, 3, 9, 8, 6, 9, 7, 5, 3, 10, 4, 5, 13, 8, 6, 10, 4, 9, 15, 8, 10, 6, 9, 7, 7, 8, 5, 3, 11, 8, 6, 7, 4, 3, 7, 7, 7, 14, 10, 8, 4, 11, 6, 9, 5, 6, 12, 3, 8, 7, 7, 3] min is 1 and max is 15 final break up list is [4,67,86,38,61,95,6,17,18,34,36,39,58,64,69,77,87,90,7,8,9,10,24,26,28,30,35,41,46,48,51,52,54,62,66,68,71,74 ,78,88,92,97,1,5,11,12,16,21,25,32,33,42,45,47,53,55,63,70,72,73,79,80,83,84,85,91,94,98,99,14,15,19,20,23,2 9,31,40,43,44,49,56,59,60,65,75,81,89,93,2,13,22,27,37,50,57,76,82,96,100, ,3] THE FINAL SAMPLE GENERATED BY MHRW IS Node 4, Node 34, Node 69, Node 36, Node 87, Node 9, Node 74, Node 26, Node 45, Node 1, Node 11, Node 16, Node 47, Node 29, Node 60, Node 31 BUILD SUCCESSFUL (total time: 3 seconds) So, this was the sample nodes generated for crawling 100 nodes in OSN by MHRW. This can be done for any number of nodes which helps in generating efficient samples for crawling OSN in less time. IV.

Results

The MHRW is implemented to select the node with higher probability first with maximum chances. Firstly, a scale is created for putting the node in different range, a node with higher probability has the maximum chances of probability that maximum number of nodes are being chosen from them and the one with lower probability has chances of less nodes are selected. The scale is divided into 10 partitions for putting the value in different range. The starting range of scale contains maximum number of nodes range and as the scale reaches to the end the number of nodes becomes very less. The value of the node is computed by how densely it is connected to network. By applying formula if the value comes in between 0 and 1 then it is selected otherwise it is rejected. The probability of selecting a node is maximum in the beginning of scale and as it reaches to an end nodes selected are very less. And at the end, the nodes are collected in different partitions of the scale giving the probabilities on the basis of which nodes are selected for crawling. The scale gives the probability of selecting nodes and so the range having maximum percentage of nodes will select highest number of nodes and as the percentage decreases the number of nodes to be selected will be less. The study gives the efficient way of crawling OSN by the MHRW technique. V.

Database Tables

The database tables which were used for storing the data are described as follows. Each table contains its required attributes. The screenshots of the tables are: Table No. 1 City Colu mn Na me

Data Type

Nullable

Default

CITYID

NUMBER

CITYNAME

VARCHAR2(4000)

Yes

Primary Key

Table No. 2 Institutions

Colu mn Na me

Data Type

Nullable

Default

EDUID

NUMBER

Yes

Primary Key -

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EDUINSTITUTE

VARCHAR2(4000)

Yes

Table. 3 Link

Colu mn Na me

Data Type

Nullable

Default

Primary Key

TOPIC

VARCHAR2(50)

SEQ

NUMBER

INFO

VARCHAR2(80)

Yes

Table No. 4 Node

Colu mn Na me

Data Type

Nullable

Default

Primary Key

NODEID

NUMBER

Yes

FNAME

VARCHAR2(4000)

Yes

INAME

VARCHAR2(100)

Yes

HOMETOWNID

NUMBER

Yes

HIGHSCHOOID

NUMBER

Yes

CURRENTCITYID

NUMBER

Yes

COLLEGEID

NUMBER

Yes

FAVSPORTSTEAMID

NUMBER

Yes

ORGID

NUMBER

Yes

POLITICALVIEW

NUMBER

Yes

EMAILID

VARCHAR2(4000)

Yes

PSWD

VARCHAR2(4000)

Yes

Table No. 5 Organisations

Colu mn Na me

Data Type

Nullable

Default

Primary Key

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ORGANISATIONS

NUMBER

ORGNAME

VARCHAR2(4000)

Yes

Table No. 6 Sports team name Colu mn Na me

Data Type

Nullable

Default

Teamid

NUMBER

Sportsteamname

VARCHAR2(4000)

Yes

VI.

Primary Key

Conclusions and Future Scope

In online social networks, crawling the graph efficiently is important since the graphs are very large and highly dynamic. Often, crawling an entire connected component is not feasible, and one must resort to using samples of the graph. The MHRW is implemented to select the node with higher probability first with maximum chances. Firstly, a scale is created for putting the node in different range, a node with higher probability has the maximum chances of probability that maximum number of nodes are being chosen from them and the one with lower probability has chances of less nodes are selected. The number of nodes are taken out of which the nodes will be selected for crawling. The scale is divided into 10 partitions for putting the value in different range. The starting range of scale contains maximum number of nodes range and as the scale reaches to the end the number of nodes becomes very less. The value of the node is computed by how densely it is connected to network. By applying formula if the value comes in between 0 and 1 then it is selected otherwise it is rejected. The probability of selecting a node is maximum in the beginning of scale and as it reaches to an end, nodes selected are very less. And at the end, the nodes are collected in different partitions of the scale giving the probabilities on the basis of which nodes are selected for crawling. The study gives the efficient way of crawling OSN by the MHRW technique. Further techniques will be introduced which will produce more efficient results and make the crawling process in OSN to be traversed in no time. Social networking analysis has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods, social network analysis software, and researchers. There are various measurement techniques for analyzing the efficiency, user behavior, searching of social networking sites. Online social networking is still very much in its infancy, yet it already forms the basis for some enormously popular applications. References 1. 2.

3. 4. 5. 6 7. 8.

Dr. Sanjeev Dhawan, Dr. Kulwinder Singh and Kirti Saini,”Comparing Crawling Techniques in Social Network” IJIRS, Vol. 3 Issue 7, pp. 216-223. Tianyi Wang, Yang Chen, Zengbin Zhang, Tianyin Xu, Long Jin, Pan Hui, Beixing Deng, Xing Li,” Understanding Graph Sampling Algorithms for Social Network Analysis”, 2011 31st International Conference on Distributed Computing Systems Workshops Rev. 41, 401402. Junjie Tong, Haihong E , Meina Song and Junde Song,” Empirical Studies on Methods of Crawling Directed Networks”, Vol. 10, Issue 1, No 1, January 2013. Yuming Mai ,” Uniform Sampling of the Facebook Social Network Using the Metropolis Hasting Random Walk” ACM Transactions on Applied Perception, Vol. 2, No. 3, Article 1, 2010 Minas Gjoka, Maciej Kurant,Carter T. Butts and Athina Markopoulou,” A Walk in Facebook: Uniform Sampling of Users in Online Social Networks”,(2011).. Rasti, A., Torkjazi, M., Rejaie, R., Duffield, N., Willinger, W., And Stutzbach, Respondent-driven sampling for characterizing Unstructured overlays, INFOCOM 2009, IEEE, 27012705. Tianyi Wang, Yang Chen, Zengbin Zhang, Tianyin Xu Long Jin, Pan Hui, Beixing Deng, Xing Li.” Understanding Graph Sampling Algorithms for Social Network Analysis”, 2011 31st (IEEE) Rev. 41, 401402. Alan E. Mislove,” Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems”,2009.

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Overlapping Community Detection using Label Sharing Approach 1,2

Dr. Kulvinder Singh1, Dr. Sanjeev Dhawan2, Vinay3 Faculty of Computer Science & Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra-136119, Haryana, INDIA 3 Research Scholar, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra-136119, Haryana, INDIA

Abstract: Community detection is a crucial task in social network analysis. Social interactions exist at intervals some social context and communities are a basic kind of social contexts. Communities are intuitively characterized as unusually densely connected subsets of a social network. This notation becomes a lot of problematic if the social interactions modification over time. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only one kind of relation in the network, and moreover, the mining results are independent of the usersâ&#x20AC;&#x2122; needs or preferences. The interplay between social interacts and social contexts are crucial to know the evolution of networks. Thus, it's necessary to each notice communities and tracks their changes. According to the past work related to community detection there were so many algorithms or methods which were successful in detection of communities. In this paper, we develop a new method (label sharing method) with the help of which we will detect communities. In this method, we made a separate table which has the list of communities present in the network and the number of members belonged to these communities. Keywords: community, community detection, label sharing approach, social networks. I. Introduction Social networks are playing a very important role in the present conditions as everyone is running behind success by creating relationships with each other. Social networks provide a base to the users to communicate with the people of their interest with the help of communities. Community is a term which has no proper definition but for ease we can define community as it is a group of people with common features or interests. These interests can be a favorite cricket team, a college, a country or follower of any politician. Users belonging to the same community have strong understanding or relationship as compare to those users which are present in different communities. We have studied a lot about social networks and communities like social networks consists of communities and the communities are the group of users of same interest but the main problem is how to find out that in which community the users present or who are the users present in a specific community. Many researchers have worked on it and they gave their idea about community detection by means of many algorithms or methods. Some of the popular methods were as follows: label propagation algorithm, clique based method for community detection. Some methods which were successful in the detection of community have some disadvantages also. Drawbacks which we understand or faced are like: some methods failed in detection of overlapping communities and which gain success in finding of overlapping communities take so much time. Before discussing our method we would like to define overlapping communities. Overlapping communities consist of a group of people which is present in more than one community. We will try to detect these overlapping communities by a new approach named as label sharing approach. In this approach we made a separate table which maintains the record of the number of communities present in a social network and also the list of the members belongs to each community. The whole working of this approach we will discuss in the working section. II. Related Work Ding Xiao et al. [2] advised that consistently analyse the matter of mining hidden communities on heterogeneous social networks supported the observation that completely different have relations and different importance with reference to a definite question, the author propose a replacement methodology for learning an optimum linear combination of those relations which may best meet the userâ&#x20AC;&#x2122;s expectation. With the obtained relation, higher performance may be achieved for community mining. The authors approach to social network analysis and community mining represents a significant shift in methodology from the standard one a shift from single-network, user-independent analysis to multi-network, user-dependant and query-based analysis.

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Experimental results on Iris information set and DBLP information set demonstrate the effectiveness of our methodology. Guilan Hu et al. [3]. The authors advised that private info mining will resolve the hidden relationship and characteristics of the target individuals which may be used for active post operation. The initial options that were enclosed within the personal info data typically have high dimension and redundancy which regularly drags down data processing potency. A feature optimisation methodology is planned here to resolve the matter. The strategy with the aim of knowledge spatiality deduction relies on effective association of rough pure mathematics with PCA approach. The experimental results demonstrate that the hybrid feature optimisation methodology is effective in up classification accuracy. L. Sørensen [4] advised that social networks are the very best growing internet application in terms of users. Totally different surveys show that users are most involved with their privacy in reference to web-based social networks. Anyhow uses compete within the variety of friends they'll attach to their own profile. This suggests that the trust relations user area unit mistreatment to determine friends within the internet applications becomes considerably totally different from the trust relations utilized in face-to-face conferences. This paper compared and mentioned a number of the prevailing self-management mechanisms in trust in 3 of the foremost used web based mostly social networking applications and suggests totally different aspects for handling trust from a user-centric style perspective. Steve Gregory [5] planned an algorithmic program for locating overlapping community structure in terribly giant networks. The algorithmic program relies on the label propagation technique of Raghavan, Albert, and Kumara, however is ready to notice communities that overlap. The author’s main contribution was to increase the label and propagation step to incorporate info regarding over one community: every vertex will currently belong to up to v communities, wherever v is that the parameter of the algorithmic program. The planned algorithmic program conjointly handles weighted and bipartite networks. It conjointly in no time and may method terribly giant and dense networks in an exceedingly short time. During this work every vertex can't be updated severally that is that the main downside of this analysis. U Kang et al. [7] introduced GIM-V a vital primitive that Pegasus uses for its algorithms to investigate structures of huge graphs. The author conjointly introduced HEigen an oversized scale Eigen thinker that is additionally a district of Pegasus. each GIM-V and HEigen were extremely optimized achieving linear proportion on the quantity of machines and edges and providing nine.2x and 76x quicker performance than their naïve counterparts severally. The authors analyzed terribly giant world graphs with billions of nodes and edges mistreatment Pegasus. Lovro Subelj et al. [8] planned a quick methodology for detection of communities in giant advanced networks i.e. Label propagation methodology. The author gave a sophisticated label propagation algorithmic program that creates a hybrid technique for community formation, namely, defensive preservation and offensive enlargement of communities. 2 ways were collaborated in an exceedingly ranked manner, to recursively extract the core of the network. The algorithmic program was evaluated on 2 categories of benchmark networks with planted partition and on nearly twenty five real-world networks starting from networks with tens of nodes to networks with many tens of ample edges. It absolutely was compared to this progressive community detection algorithms and superior to any or all previous label propagation algorithms, with comparable time complexness. S. Simranjit et al. [9] planned a system mistreatment neural network and diversified weights supported multilayer text extraction, frequency of communication for friend recommendation from friends of friends. The authors have taken under consideration varied factors which can assign score to his friends of friends and can suggest them a lot of expeditiously. The results weren't reliable and correct that could be a limitation to the present work. A genetic based approach to find communities in social networks has been planned by Brian poet et al. [10]. The algorithmic program optimized a straightforward however efficacious fitness operate ready to establish densely connected teams of nodes with thin connections between teams. This methodology was economical as a result of the variation operators were changed to require into thought solely the particular correlations among the nodes therefore reasonably reducing the analysis house of potential solutions. The most downside of this system was it doesn't notice part connected nodes within the network. S. Thayananthporkalanchiam et al. [11] represented the matter of community detection in advanced networks as a Multi Objective clustering problem and presented a biological process Multi Objective approach to uncover community structure. The planned algorithmic program optimized 2 objective functions that were ready to establish densely connected teams of nodes having thin lay connections. The strategy generated a collection of network divisions at totally different ranked levels during which solutions at deeper levels, consisting of the next variety of modules that were contained in solutions having a lower variety of communities. optimisation methodology outlined an Objective operate that allowed the division of a graph in sub graphs and take a look at to maximise this objective so as to get the simplest partitioning of the network. III. Problem Formulated The main problem which is encountered in community detection algorithms is how to find overlapping communities. Based on past work related to community detection problem we develop an approach named as label shared approach for the detection of overlapping communities.

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IV. Working The algorithm label shared approach works in way as described below: 1. First of all we create a random social network for any number of nodes which are entered by a user who wants to login. 2. After that user is asked for entering the threshold value. Here threshold value tells the contribution of the people present in a community to that community. If the contribution is more than the threshold value then that community is neglected. 3. After entering these two terms, a social network is generated and a random user who wants to check the suggestions from the different communities is asked to enter the email id and password. 4. After entering username and password, a list of friends is suggested from different communities. 5. If a person is suggested from more than one community it means that person is present in two or more communities. So, in this way we overlapping communities are detected. The output produced from this approach will look like as shown in the figures below:

Fig. 1 list of suggestions Message of overlapping community detection is shown below:

Fig. 2 overlapping message V. Results On behalf of working and number of nodes suggested we generate two results which are described as follows:

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Firstly we fix the threshold value and the social network is generated for different number of nodes says 100, 200, 300 and so on. Suppose we fix a threshold value 12. The graph shown below on behalf of this shows that how the number of suggestions from each communities changes.

Fig.3 number of suggestions for each community varies for different number of nodes in a social network for a fixed threshold. 2.

Second result based on a situation that we fix the nodes in a network and changes the threshold value. So the results produced are shown with the graph shown below in which we will notice how the number of suggestions changes for each community.

Fig. 4 number of suggestions for each community varies for different threshold value in a social network for fixed number of nodes. These are the two results which are produced in our research and the output produced by our approach shows that overlapping communities are detected successfully. VI. Conclusion and Future Scope Our research is based on the detection of overlapping communities with the help of label sharing approach. We described earlier that in this approach we made a separate table which maintains the record of all the communities present in the network and also the number of nodes belonged to those communities. A number of members are suggested from each community on the behalf of the members’ interest. The result produced by this approach shows that overlapping communities are successfully detected. In future, we will try to improve the time for producing the output. References [1] [2]

Dr. Kulvinder Singh, Dr. Sanjeev Dhawan, Vinay, “Study of Community Detection in Social Networks using Label Sharing Approach”, International Journal of Research in Information Technology, Vol. 2, Issue 4, pp. 65- 70, 2014 Ding Xiao, Nan Du, Bin Wu and Bai Wang “Community Ranking in Social Network”, Second IEEE International Multisymposium on Computer and Computational Sciences, pp. 322-329, 2007

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[10] [11]

Guilan Hu, Xiaochun Cai “Research on Feature Optimization Method in Personal Information Mining”, proceedings of WASE International Conference on Information Engineering, Vol. 1, pp. 656-659, 2009 Sorensen, L. “User managed trust in Social Networking– comparing Facebook, MySpace and Linkedin”, proceedings of Wireless VITAE 1st International Conference, pp. 427-431, May 2009 Steve Gregory “Finding overlapping communities in networks by label propagation”, New Journal of Physics, Vol. 12, pp.1-26, 2010 Vinay, Sumit, Jai Parkash, “A Review Paper for Detection of of Overlapping Communities in Complex Networks”, International Journal of Computer Science and Information Technologies, Vol. 5 (3), pp. 3339-3341, 2014 U Kang and Christos Faloutsos “Big Graph Mining: Algorithms and Discoveries”, ACM SIGKDD Explorations Newsletter , Vol. 14, no. 2, pp.29-36, 2012 Lovro Subelj and Marko Bajec “Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction” Phys. Rev. E 83(3), 036103, pp:- 1- 13 March, 2011 S. Simranjit, M. Nikunj and M. NishantSardar Patel Institute of Technology, Mumbai, India “A Multifaceted approach towards Friend Recommendation in Social Network”, International Journal of Computer Science and Telecommunications Volume 3, Issue 11, November 2012 Brian Dickinson, Benjamin Valyou “A Genetic Algorithm for Identifying Overlapping Communities in Social Networks Using an Optimized Search Space”, Social Networking, Vol. 2, pp. 193-201, 2013 S. Thayananthporkalanchiam, Mrs. G. Umarani Srikanth, S. Rajakuncharam, Veeraraghavapuram “MOGA-Net: A MultiObjective Genetic Algorithm-Net to Find Communities in Complex Networks”, International Journal of Computer Science and Management Research, Vol. 2 Issue 2, pp. 1601-1606, 2013

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A Generalize Formula for Parabolic Partial Differential Equation (PPDE) Using Matrix Analysis Md.Sahidul Islam1, Md. Reduanul Alam2 Department of Electronics and Communication Engineering East West University Aftabnagar, Dhaka-1212, Bangladesh 2 Department of Mathematics University of Chittagong Chittagong-4338, Bangladesh

Abstract: In this paper we consider the generalize formula for Parabolic Partial Differential Equations (PPDE) using matrix analysis. Actually it is an improvement of generalize scheme. The generalize scheme combines three formula (one explicit and two implicit).Our proposed generalize formula combines five formulae (two explicit and three implicit). Keywords: explicit and implicit method; toeplitz matrix; tridiagonal linear system; convex combination I. Introduction Different numerical methods are used for solving Parabolic Partial Differential Equation (PPDE). The numerical solution of PPDE in finite difference method can be solved by using explicit and implicit techniques. Consider the parabolic partial differential equation with initial condition and the boundary conditions and (1.1) We can solve the PPDE via matrix [1] analysis. Let us rewrite the most general PPDE in matrix form. We observe that the decomposition of plane consists of two stages; first, spatial decomposition (setting ), and second, temporal decomposition (setting ).

Figure 1: Solution domain for initial value problem Next we evaluate the partial differential at an arbitrary grid point .Replace the derivative on the right hand of the equation by its second-order central difference approximation. We denote be the semi discrete approximation to the function , (1.2) For

, these equations are supplemented by the boundary conditions . The initial value problem for the can be easily be written in the matrix form. Let;

and ;

and

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In most cases, is called Toeplitz matrix. The order of With these definitions, equation (1.2) becomes(1.3)

depends on the space decomposition.

To solve this PPDE (1.3), we use different finite difference explicit and implicit techniques. In this study we have proposed a generalize formula which combines five different finite difference methods. II. Explicit Method A. Schmidt method For Schmidt [1], [5] method evaluate the equation (1.3) at the time level, approximation for the time derivative, we getand Solving the previous equation for

and use first forward difference

; where

yields-

(1.4)

; where,

This is an explicit equation for computing the approximate solution at one time level from the values of the solution at the previous time level with known from initial condition. We can march forward one increment of at a time. The matrix is called evolution matrix for the numerical method. B. Richardson`s method In the Richardson`s [1], [7] method we evaluate the equation at the time level difference approximation for the time derivative. Then equation (1.3) becomes

which gives(1.5)

and we use central

; where,

This is another explicit formula. This formula has a special starting procedure. To use this formula we need the solution at time level which we get from Schmidt method. III. Implicit Method A. O`Brien Method This method [1], [6] is obtained evaluating (1.3) at time level forward difference approximation for the time derivative. We get-

Re-arranging terms in the evolution equation, we get (1.6)

-th,

and using the first order

; where

We see that O`Brien method defines implicitly in term of . Since is a tridiagonal matrix, computing from the equation (1.5) requires only about twice as many algebraic operation as computing (1.6). B. Crank-Nicolson method In Crank-Nicolson [1], [6] method the space derivative is replaced by the average of the matrix in (1.3) taken along and -th level and time derivative is replaced by forward difference this yield

Then we have (1.7)

; where

This is another implicit formula requiring the solution of a Tridiagonal linear system at each time step. C. Burnett method In Burnett method [1], [2], first forward difference for the time derivative and the space derivative is replaced by the average [Multiplying the matrix in (1.3) taken along by and adding it with the matrix in (1.3) taken along -th level] of and - th level evaluates (1.3). We have-

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This yield–

(1.8)

; where

This is another implicit formula requiring the solution of a tridiagonal linear system at each time step. IV. Generalization of Finite Difference Formula We derive the generalization of finite difference formulae for PPDE with a unique general formula via matrix analysis as follows(1.9) where, , are two weighting factors and . Now, if then (1.9) Schmidt formula if then (1.9) Richardson`s formula if then (1.9) Crank-Nicolson formula if then (1.9) O`Brien formula if then (1.9) Burnett formula For the matrix analysis putting in (1.9), then we get-

Figure 2: Stencil for the generalization formula It is interesting matter explicity or implicity of generalization formula depends on the value of . If then the formula (1.9) is explicit and for , the formula (1.9) is implicit. V. Conclusion Here we have used the convex combination property for the time between -th and -th level and for the space between -th and -th (see the figure 2). If the two weighting factor and lay outside the interval then our generalization formula for PPDE is not valid. The generalize formula is also not valid if lies outside the interval . References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8].

B. Bradie , “A Friendly Introduction to Numerical Analysis”, Pearson Education, Inc., 1st ed., 2007. Burnett, David S, “Finite Element Analysis,” From Concepts to Applications, Reading, M.A, Addison-Wesley, 1987. C.F.Gerald and P.O.Whealty, “Applied Numerical Analysis”, Pearson Education Asia, pp-528,6ed, 2002. E. A. Schimdt, “Foppl Festschrift,” Springer Verlag OHG, Berlin, 1924. G.G. O’Brien, M.A.Hyman and Kaplan, S. J. Math. Phys., Vol no 29, pp223.1951. J.Crank and P Nicolson, “A Practical Method for Numerical Evaluation of Solution of Partial Differential Equation of the Heat Conduction type,” Proc of the Cambridge Philosophical Society, vol no 43, pp50-67.1947. L. F Richardson, Math. Gaz., Vol no 12, pp 415, 1925. William F.Ames, “Numerical Method for Partial Differential Equations,” Academic Press, Inc, 2nd edition, 1977.

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American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

MEASURING THE FUZZINESS OF PRACTICAL DISTRIBUTED FUZZY SETS J Mary Gracelet1, Dr. G Velammal2 Associate Professor, Department of Mathematics Sri Meenakshi Government Arts College for Women Madurai 625 002, India (1,2)

Abstract: Membership function of a fuzzy set can be generalized using distribution or generalized function. For distributed fuzzy sets defined over intervals on the real line, the membership functions can be generalized using distributions. The practical distributed fuzzy sets are defined by specifying a partition of [a,b] and the average membership value mi where 0  mi  1 . The operations of the usual fuzzy sets are extended to PDFuS also. In this paper we introduce the concept of relative index of fuzziness for a PDFuS Keywords: distributed fuzzy sets, fuzzy sets, generalized functions, measure of fuzziness, membership functions, practical distributed fuzzy sets, relative index of fuzziness. I.

INTRODUCTION

In 1965, Lotfi A. Zadeh[4] introduced the concept of fuzzy sets. The membership function of an usual fuzzy set is a function from a set to the interval [0,1]. This can be generalized in several ways. For example, an L Fuzzy set has a membership function where the range is a partially ordered set [2]. In our paper [5], we had introduced the concept of distributed fuzzy sets which are defined over intervals on the real line. For a distributed fuzzy set membership function can be a generalized function or a distribution. In our previous paper [6], we had introduced the concept of practical distributed fuzzy sets. The practical distributed fuzzy sets differ from the usual fuzzy sets in a crucial manner. In an ordinary fuzzy set the membership function is known at every point while in a PDFuS only the average membership value over certain intervals is known. However we have seen that [7] intersection, union, aggregation can be defined for the PDFuS in exactly the same manner as in the case of fuzzy set. So what is the difference between these two? The answer is that the PDFuS is vaguer than the fuzzy set. So while using PDFuS in applications of fuzzy logic we have to take this into account. So first there must be a way of measuring the fuzziness of a PDFuS. For fuzzy sets several measures of fuzziness have been defined. These must be suitably modified for the PDFuS. As an example consider a PDFuS on the interval [0, 4] with the average membership value 1/4. If we take μ(x) = 1/4 on the interval then the index of fuzziness in terms of Euclidean distance would be where μc (x) = 0 if μ(x) ≤ 1/2 and μ c(x) = 1 if μ(x) > 1/2. So on calculation the index is 1/2. So for a fuzzy set with μ(x) = 1/4 on the interval [0, 4], the index of fuzziness (Euclidean) would be 1/4. However it would not be meaningful to assign the same index number to a PDFuS with the average membership value 1/4. This PDFuS may correspond to the crisp set [x, x+1] for any x such that 0 ≤ x ≤ 3. Or the PDFuS may represent a fuzzy set whose membership function μ(x) satisfies =1. So while attempting to measure the fuzziness of a PDFuS this additional type of vagueness or uncertainty must be taken into account. So we define a new index number called the relative index of fuzziness. First the axioms and definitions corresponding to the measures of fuzziness [7] of usual fuzzy sets are listed. Then the concept of relative index of fuzziness of a PDFuS is introduced. II. PRELIMINARIES We briefly recall the definition of the practical distributed fuzzy sets. Consider a sequence of functions in with the following properties with respect to the interval [a,b].

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P1 :

out side

P2 :

P3 : 0 ≤ It is well known that such a sequence exists. Definition 2.1: Let be a sequence of functions in satisfying P1, P2, and P3 with respect to the interval [a, b] and let T be a distribution. T is said to define a distributed fuzzy set on [a, b] if i. T is a positive distribution ii.

lim

iii.

the P1, P2, and P3 with respect to the interval [a,b] . lim T ( f n ) ≤ b-a

n 

exists and is the same for any sequence of functions

satisfying

n

Definition 2.2: Suppose T defines a distributed fuzzy set over an interval [a, b]. Then it is said to have average membership value m over the interval [a, b] where The average membership value of T over the interval [a,b] will be denoted by

AvmgT ([a, b]) .

Definition 2.3: A Practical distributed fuzzy set A on an interval [a, b] is defined by specifying i. a partition a  a0  a1  a2  ...........  an  b and ii.

the average membership value

mi in each interval

The average membership value of A in the interval

where mi lies between 0 and 1.

is denoted by

III. MEASURES OF FUZZINESS FOR FUZZY SETS Definition 3.1: A measure of fuzziness is a function f: P(X) ->R where P(X) denotes the set of all fuzzy subsets of X. That is the function f assigns a value to each fuzzy subset of X which characterizes the degree of the fuzziness of A. Definition 3.2: Every measure of fuzziness must satisfy axiom 1:

= 0 if and only if A is a crisp set.

axiom 2: If A and B are two fuzzy subsets of X, then

≤

whenever A is less fuzzy than B.

Definition 3.3: For an ordinary fuzzy set A, a measure of fuzziness called the index of fuzziness is defined in terms of a metric distance (Hamming or Euclidean) of A from any of the nearest crisp set for which 0 if µA(x) ≤ ½ µc(x)

= 1 if µA(x) > 1/2

Definition 3.4 When Hamming distance is used the measure of fuzziness is defined by, b

f ( A)  

( x) 

( x) dx

Definition 3.5: For Euclidean distances, measure of fuzziness is given by,

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Definition 3.6: For Minkowski’s class of functions, the index of fuzziness is defined by,

IV. MEASURES OF FUZZINESS FOR PDFuS Definition 4.1: Let I be an interval and P be a partition of I. Let A be a PDFuS defined on (I, P). Let X be the collection of all such (I,P,A) and f be a function from X to the set of all non-negative real numbers. Then to be a good measure of fuzziness of a PDFuS f should satisfy: axiom 1:

= 0 if and only if A is a crisp set

axiom 2: If A and B are fuzzy sets defined on the same interval I then of I if B is considered to be fuzzier than A.

≤

for some partition P

Note: Of course axiom 2 depends on the interpretation of "fuzzier". Definition 4.2: Let A be a distributed fuzzy set defined on an interval I = [a,b], then the index of fuzziness for the DFuS is defined by : where, 0 if m ≤ ½ µc(m) = 1 if m > ½ and m is the average membership value of T over the interval [a,b], Then

f ( I , A) = [|m-µc(m)|w (b-a)]1/w = |m-µc(m)| (b-a)1/w Definition 4.3: Suppose we have a PDFuS A defined over a partition a0<a1<………<an with the average membership value mk on the interval Ik = [ak 1 , ak ] . Suppose that within interval [ak-1,ak] it is desired to know the average membership value on subintervals of length

k ( that is k is the desired accuracy in Ik). Then the

relative index of the fuzziness is given by where, µc(mk)

if mk ≤ ½

if mk > ½

Example 4.1: Consider a PDFuS A on I=[0,5] and let P={0,1,2,3,4,5} be a partition on I. Let m1=0.5, m2=0.7and m3=0.1, m4=0.9, m5=0.2 and let the desired accuracy k =0.5. Then the relative index of fuzziness (Euclidean) is given by, where w=2. Hence

|0.5-0|(1/0.5)+|0.7-1|(1/0.5)+|0.1-0|(1/0.5)+|0.9-1|(1/0.5)+|0.2-0|(1/0.5) = (0.5+0.3+0.1+0.1+0.2)(1/0.5) = 2.4

Note(i):The relative index of the fuzziness(Minkowski) satisfies the axiom1: By definition 4.3, Hence f (I,P,A) = 0 if and only if That is f (I,P,A) = 0 if and only if

for every k. for every k.

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That is f (I,P,A) = 0 if and only if mk = 1 or 0 for every k. That is f (I,P,A) = 0 if and only if A is a crisp set. Hence axiom 1 is satisfied. (ii) The Minkowski index of fuzziness may not satisfy axiom 2.

V. CONCLUSION In this paper we have introduced some measures for measuring the fuzziness of the PDFuS. All measures of fuzzy sets can be suitably modified for the PDFuS also. Any PDFuS can be treated as an ordinary fuzzy set, where membership function is constant in each subinterval of the partition. Hence PDFuS can be used in fuzzy logic just like any ordinary fuzzy set. However while verifying the validity of the conclusion we have to look into the relative index of fuzziness of the PDFuS. Hence the concept introduced in this paper is very useful in application of PDFuS in fuzzy logic. REFERENCES [1]

I.M.Gelfand and G.E.Shilov,”Generalized Functions”,Academic Press,1964.

[2]

George J.Klir, Bo Yuan, “ Fuzzy sets and Fuzzy logic: Theory and applications”,PHI Learning Privare Ltd., New Delhi, 2012.

[3]

L.Schwartz,”Theorie des distributions”, Herman Paris,1966.

[4]

L.A Zadeh,” Fuzzy Sets”, Information and Control,l8,1965,pp 338-353.

[5]

G.Velammal and J.Mary Gracelet,”Generalizing the concept of membership function of fuzzy sets on the real line using Distribution Theory”,AIJRSTEM 14-112, 2014,pp 21-25.

[6]

Mary Gracelet J, Dr.G Velammal,”Practical Distributed Fuzzy Sets”,IOSR-JM Volume 10,Issue3 VER .V(May-June), 2014, pp96-99.

[7]

George J. Klir and Tina A Folger, “Fuzzy Sets,Uncertainty and Information”, Prentice Hall,2005.

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